Land-cover classification with high-resolution remote sensing images using transferable deep models
In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often d...
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| Vydáno v: | Remote sensing of environment Ročník 237; s. 111322 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Elsevier Inc
01.02.2020
Elsevier BV |
| Témata: | |
| ISSN: | 0034-4257, 1879-0704 |
| On-line přístup: | Získat plný text |
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| Abstract | In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-cover classification with high-resolution and heterogeneous remote sensing images. In this paper, we propose a scheme to apply deep model obtained from labeled land-cover dataset to classify unlabeled HRRS images. The main idea is to rely on deep neural networks for presenting the contextual information contained in different types of land-covers and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models. More precisely, a deep Convolutional Neural Networks (CNNs) is first pre-trained with a well-annotated land-cover dataset, referred to as the source data. Then, given a target image with no labels, the pre-trained CNN model is utilized to classify the image in a patch-wise manner. The patches with high confidence are assigned with pseudo-labels and employed as the queries to retrieve related samples from the source data. The pseudo-labels confirmed with the retrieved results are regarded as supervised information for fine-tuning the pre-trained deep model. To obtain a pixel-wise land-cover classification with the target image, we rely on the fine-tuned CNN and develop a hybrid classification by combining patch-wise classification and hierarchical segmentation. In addition, we create a large-scale land-cover dataset containing 150 Gaofen-2 satellite images for CNN pre-training. Experiments on multi-source HRRS images, including Gaofen-2, Gaofen-1, Jilin-1, Ziyuan-3, Sentinel-2A, and Google Earth platform data, show encouraging results and demonstrate the applicability of the proposed scheme to land-cover classification with multi-source HRRS images.
•A method to learn transferable deep model for 5-class land-cover (LC) classification.•A labeled dataset consisting of 150 Gaofen-2 images for LC classification.•It improves LC classification performance about 20% using multi-source RS images.•The method shows good transferability on different sensors and geolocations. |
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| AbstractList | In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-cover classification with high-resolution and heterogeneous remote sensing images. In this paper, we propose a scheme to apply deep model obtained from labeled land-cover dataset to classify unlabeled HRRS images. The main idea is to rely on deep neural networks for presenting the contextual information contained in different types of land-covers and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models. More precisely, a deep Convolutional Neural Networks (CNNs) is first pre-trained with a well-annotated land-cover dataset, referred to as the source data. Then, given a target image with no labels, the pre-trained CNN model is utilized to classify the image in a patch-wise manner. The patches with high confidence are assigned with pseudo-labels and employed as the queries to retrieve related samples from the source data. The pseudo-labels confirmed with the retrieved results are regarded as supervised information for fine-tuning the pre-trained deep model. To obtain a pixel-wise land-cover classification with the target image, we rely on the fine-tuned CNN and develop a hybrid classification by combining patch-wise classification and hierarchical segmentation. In addition, we create a large-scale land-cover dataset containing 150 Gaofen-2 satellite images for CNN pre-training. Experiments on multi-source HRRS images, including Gaofen-2, Gaofen-1, Jilin-1, Ziyuan-3, Sentinel-2A, and Google Earth platform data, show encouraging results and demonstrate the applicability of the proposed scheme to land-cover classification with multi-source HRRS images. In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-cover classification with high-resolution and heterogeneous remote sensing images. In this paper, we propose a scheme to apply deep model obtained from labeled land-cover dataset to classify unlabeled HRRS images. The main idea is to rely on deep neural networks for presenting the contextual information contained in different types of land-covers and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models. More precisely, a deep Convolutional Neural Networks (CNNs) is first pre-trained with a well-annotated land-cover dataset, referred to as the source data. Then, given a target image with no labels, the pre-trained CNN model is utilized to classify the image in a patch-wise manner. The patches with high confidence are assigned with pseudo-labels and employed as the queries to retrieve related samples from the source data. The pseudo-labels confirmed with the retrieved results are regarded as supervised information for fine-tuning the pre-trained deep model. To obtain a pixel-wise land-cover classification with the target image, we rely on the fine-tuned CNN and develop a hybrid classification by combining patch-wise classification and hierarchical segmentation. In addition, we create a large-scale land-cover dataset containing 150 Gaofen-2 satellite images for CNN pre-training. Experiments on multi-source HRRS images, including Gaofen-2, Gaofen-1, Jilin-1, Ziyuan-3, Sentinel-2A, and Google Earth platform data, show encouraging results and demonstrate the applicability of the proposed scheme to land-cover classification with multi-source HRRS images. •A method to learn transferable deep model for 5-class land-cover (LC) classification.•A labeled dataset consisting of 150 Gaofen-2 images for LC classification.•It improves LC classification performance about 20% using multi-source RS images.•The method shows good transferability on different sensors and geolocations. |
| ArticleNumber | 111322 |
| Author | Lu, Qikai Li, Shengyang Tong, Xin-Yi Zhang, Liangpei You, Shucheng Shen, Huanfeng Xia, Gui-Song |
| Author_xml | – sequence: 1 givenname: Xin-Yi surname: Tong fullname: Tong, Xin-Yi organization: State Key Laboratory LIESMARS, Wuhan University, China – sequence: 2 givenname: Gui-Song surname: Xia fullname: Xia, Gui-Song email: guisong.xia@whu.edu.cn organization: State Key Laboratory LIESMARS, Wuhan University, China – sequence: 3 givenname: Qikai surname: Lu fullname: Lu, Qikai organization: Electronic Information School, Wuhan University, China – sequence: 4 givenname: Huanfeng surname: Shen fullname: Shen, Huanfeng organization: School of Resource and Environmental Sciences, Wuhan University, China – sequence: 5 givenname: Shengyang surname: Li fullname: Li, Shengyang organization: Key Laboratory of Space Utilization, Tech. & Eng. Center for Space Utilization, Chinese Academy of Sciences, China – sequence: 6 givenname: Shucheng surname: You fullname: You, Shucheng organization: Remote Sensing Department, China Land Survey and Planning Institute, China – sequence: 7 givenname: Liangpei surname: Zhang fullname: Zhang, Liangpei organization: State Key Laboratory LIESMARS, Wuhan University, China |
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| Cites_doi | 10.1109/TGRS.2011.2168534 10.1109/LGRS.2012.2227297 10.1016/j.isprsjprs.2016.01.004 10.1109/MCI.2006.1626490 10.1109/TPAMI.2002.1017623 10.1109/JPROC.2012.2211551 10.1016/j.isprsjprs.2009.06.004 10.3390/rs70505611 10.1109/MGRS.2017.2762307 10.1109/TGRS.2015.2496185 10.1007/s11263-009-0312-3 10.3390/rs8070555 10.1109/TGRS.2014.2377785 10.1016/j.isprsjprs.2003.10.002 10.1109/LGRS.2015.2439696 10.1016/j.rse.2009.02.014 10.1080/01431161.2017.1399472 10.1016/j.rse.2018.06.034 10.3390/rs9101030 10.3390/rs5116026 10.1080/10106048709354084 10.1080/2150704X.2017.1375610 10.1109/TSMC.1973.4309314 10.1080/01431161.2013.845925 10.1016/j.rse.2014.02.015 10.1109/JSTARS.2016.2582921 10.1016/0034-4257(92)90011-8 10.1109/TGRS.2016.2612821 10.5194/isprsarchives-XL-7-W3-45-2015 10.1109/TSMCB.2009.2037132 10.1016/j.isprsjprs.2017.06.001 10.1109/LGRS.2017.2691013 10.1038/nature14539 10.1016/j.rse.2018.04.050 10.1109/LGRS.2017.2763738 10.1109/JSTARS.2016.2646138 10.1080/01431160600702632 10.1109/TPAMI.2015.2389848 10.1016/j.rse.2015.12.023 10.1080/01431160050144947 10.1109/JSTARS.2015.2449738 10.1109/LGRS.2008.916070 10.1109/JPROC.2012.2197589 10.1007/s11263-013-0620-5 10.1109/TPAMI.2017.2699184 10.3390/rs8030259 10.1007/s11390-017-1754-7 10.1109/TGRS.2011.2105490 10.1016/j.landurbplan.2006.11.009 10.1109/LGRS.2009.2015341 10.1080/0143116021000035021 10.1109/MGRS.2016.2548504 10.1016/j.rse.2015.12.041 10.1080/2150704X.2015.1062157 10.1109/TGRS.2015.2400449 10.1109/TGRS.2017.2692281 10.1109/LGRS.2015.2499239 10.1007/s11119-012-9274-5 10.1109/TGRS.2012.2192740 10.3390/rs71114988 10.1016/j.isprsjprs.2017.07.014 10.1016/j.rse.2011.11.020 10.1109/TIP.2017.2726182 10.1109/TGRS.2014.2305805 10.1109/TGRS.2017.2685945 10.1109/TGRS.2014.2306692 10.1109/TPAMI.2016.2644615 10.1109/TGRS.2006.877950 10.1109/LGRS.2017.2681128 10.3390/rs71114680 10.1109/TGRS.2006.875360 10.1109/LGRS.2016.2521418 10.1109/TGRS.2013.2249522 10.1016/j.isprsjprs.2011.08.002 10.1109/TGRS.2016.2616585 10.1109/LGRS.2010.2047711 10.1023/B:VISI.0000022288.19776.77 10.1109/TGRS.2004.842478 10.1109/TGRS.2009.2019636 10.1016/j.rse.2010.12.017 10.1016/S0304-3800(03)00139-X 10.1109/TGRS.2006.876704 10.3390/rs9060522 |
| ContentType | Journal Article |
| Copyright | 2019 Elsevier Inc. Copyright Elsevier BV Feb 2020 |
| Copyright_xml | – notice: 2019 Elsevier Inc. – notice: Copyright Elsevier BV Feb 2020 |
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| DOI | 10.1016/j.rse.2019.111322 |
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| Keywords | Deep learning High-resolution remote sensing land-cover classification Gaofen-2 satellite images |
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| References | Shao, Lunetta, Wheeler, Iiames, Campbell (bib71) 2016; 174 Shao, Yang, Xia (bib70) 2013; 34 Persello, Bruzzone (bib68) 2014; 52 Uijlings, Van De Sande, Gevers, Smeulders (bib81) 2013; 104 Yang, Crawford (bib93) 2016; 9 Krizhevsky, Sutskever, Hinton (bib38) 2012 Chen, Papandreou, Kokkinos, Murphy, Yuille (bib14) 2018; 40 Napoletano (bib59) 2018; 39 Yu, Yang, Xia, Liu (bib95) 2016; 8 Zhao, Zhong, Xia, Zhang (bib102) 2016; 54 Zhong, Wu, Zhao (bib105) 2017; 9 Deng, Dong, Socher, Li, Li, Fei-Fei (bib17) 2009 Gómez-Chova, Camps-Valls, Munoz-Mari, Calpe (bib24) 2008; 5 Xia, Delon, Gousseau (bib85) 2010; 88 Huang, Zhao, Song (bib33) 2018; 214 Paisitkriangkrai, Sherrah, Janney, van den Hengel (bib66) 2016; 9 Zhao, Du (bib103) 2016; 113 Duro, Franklin, Dubé (bib18) 2012; 118 Maggiori, Tarabalka, Charpiat, Alliez (bib51) 2017; 55 Wu, Yap (bib84) 2006; 1 Marmanis, Datcu, Esch, Stilla (bib52) 2016; 13 Zhang, Huang, Huang, Li (bib100) 2006; 44 Shi, Chen, Bi, Chen, Yu (bib74) 2015; 12 Zhong, Zhao, Zhang (bib106) 2014; 52 Zhang, Sargent, Pan, Li, Gardiner, Hare, Atkinson (bib99) 2018; 216 Fauvel, Tarabalka, Benediktsson, Chanussot, Tilton (bib19) 2013; 101 Demir, Minello, Bruzzone (bib16) 2014; 52 Hu, Xia, Hu, Zhong, Xu (bib29) 2016; 8 Persello, Bruzzone (bib67) 2012; 50 Olofsson, Foody, Herold, Stehman, Woodcock, Wulder (bib61) 2014; 148 Lu, Ma, Xia (bib46) 2017; 8 Demir, Bovolo, Bruzzone (bib15) 2012; 50 Blaschke (bib6) 2001; 6 Xue, Liao, Carin, Krishnapuram (bib91) 2007; 8 Casals-Carrasco, Kubo, Madhavan (bib12) 2000; 21 Xia, Bai, Ding, Zhu, Belongie, Luo, Datcu, Pelillo, Zhang (bib89) 2018 Maggiori, Tarabalka, Charpiat, Alliez (bib50) 2017 Pacifici, Chini, Emery (bib64) 2009; 113 Burnett, Blaschke (bib11) 2003; 168 Xia, Hu, Hu, Shi, Bai, Zhong, Zhang, Lu (bib90) 2017; 55 Li, Zhang, Du, Zhang, Shi (bib43) 2017; 10 Zeiler, Fergus (bib96) 2014 Benz, Hofmann, Willhauck, Lingenfelder, Heynen (bib5) 2004; 58 Giada, De Groeve, Ehrlich, Soille (bib23) 2003; 24 Xia, Yang, Delon, Gousseau, Sun, Maitre (bib88) 2010 Yang, Yin, Xia (bib94) 2015; 53 Lu, Huang, Li, Zhang (bib45) 2016; 13 Mnih (bib56) 2013 Yan, Mas, Maathuis, Xiangmin, Van Dijk (bib92) 2006; 27 Ojala, Pietikäinen, Maenpaa (bib60) 2002; 24 Kussul, Lavreniuk, Skakun, Shelestov (bib39) 2017; 14 Ma, Li, Ma, Cheng, Du, Liu (bib48) 2017; 130 Zhang, Pan, Li, Gardiner, Sargent, Hare, Atkinson (bib98) 2018; 140 Jensen, Lulla (bib35) 1986; 2 Gerke, Rottensteiner, Wegner, Sohn (bib22) 2014 Myint, Gober, Brazel, Grossman-Clarke, Weng (bib58) 2011; 115 Moser, Serpico, Benediktsson (bib57) 2013; 101 Volpi, Tuia (bib82) 2017; 55 Bruzzone, Chi, Marconcini (bib9) 2006; 44 Tuia, Camps-Valls (bib78) 2016; 11 Jun, Ghosh (bib37) 2011; 49 Sherrah (bib73) 2016 Benediktsson, Palmason, Sveinsson (bib4) 2005; 43 Hu, Wu, Xia, Yu, Yang, Li, Song (bib32) 2013; 5 Xia, Liu, Bai, Zhang (bib86) 2017; 26 Hu, Xia, Zhang (bib30) 2017 Chakraborty, Balasubramanian, Sun, Panchanathan, Ye (bib13) 2015; 37 Ardila, Tolpekin, Bijker, Stein (bib1) 2011; 66 Othman, Bazi, Melgani, Alhichri, Alajlan, Zuair (bib62) 2017; 55 Paisitkriangkrai, Sherrah, Janney, Hengel (bib65) 2015 Ge, Yu (bib21) 2017; vol. 6 Zhao, Huang, Zhong (bib101) 2017; 14 Sheng, Song, Wang, Lyons, Knox, Cox, Gao (bib72) 2016; 185 Tarabalka, Fauvel, Chanussot, Benediktsson (bib76) 2010; 7 Tuia, Ratle, Pozdnoukhov, Camps-Valls (bib80) 2010; 7 Zhu, Tuia, Mou, Xia, Zhang, Xu, Fraundorfer (bib107) 2017; 5 Mattyus, Wang, Fidler, Urtasun (bib55) 2015 Izquierdo-Verdiguier, Laparra, Gomez-Chova, Camps-Valls (bib34) 2013; 10 Hu, Xia, Hu, Zhang (bib28) 2015; 7 Tarabalka, Chanussot, Benediktsson (bib75) 2010; 40 Zhang, Kovacs (bib97) 2012; 13 Persello, Stein (bib69) 2017; 14 Tong, Lu, Xia, Zhang (bib77) 2018 Lee (bib42) 2013; vol. 3 Liu, Minh Nguyen, Deligiannis, Ding, Munteanu (bib44) 2017; 9 Matasci, Volpi, Kanevski, Bruzzone, Tuia (bib53) 2015; 53 Ma, Li, Ma, Cheng, Du, Liu (bib47) 2017; 130 Blaschke (bib7) 2010; 65 Jiang, Xia, Lu, Shen, Jul (bib36) 2017; 32 Vuolo, Neuwirth, Immitzer, Atzberger, Ng (bib83) 2018; 72 Hu, Xia, Hu, Zhang (bib31) 2015; 7 Ozdarici-Ok, Ok, Schindler (bib63) 2015; 7 Zhao, Guo, Yue, Zhang, Luo (bib104) 2015; 36 Kussul, Skakun, Shelestov, Lavreniuk, Yailymov, Kussul (bib40) 2015; 40 Badrinarayanan, Kendall, Cipolla (bib3) 2017; 39 Mathieu, Freeman, Aryal (bib54) 2007; 81 Haralick, Shanmugam (bib26) 1973 He, Zhang, Ren, Sun (bib27) 2016 Gong, Marceau, Howarth (bib25) 1992; 40 LeCun, Bengio, Hinton (bib41) 2015; 521 Xia, Tong, Hu, Zhong, Datcu, Zhang (bib87) 2017 Bruzzone, Carlin (bib8) 2006; 44 Bruzzone, Persello (bib10) 2009; 47 Audebert, Le Saux, Lefevre (bib2) 2016 Felzenszwalb, Huttenlocher (bib20) 2004; 59 Maggiori, Tarabalka, Charpiat, Alliez (bib49) 2016 Tuia, Persello, Bruzzone (bib79) 2016; 4 Maggiori (10.1016/j.rse.2019.111322_bib49) 2016 Krizhevsky (10.1016/j.rse.2019.111322_bib38) 2012 Xia (10.1016/j.rse.2019.111322_bib88) 2010 Ardila (10.1016/j.rse.2019.111322_bib1) 2011; 66 Ge (10.1016/j.rse.2019.111322_bib21) 2017; vol. 6 Tuia (10.1016/j.rse.2019.111322_bib78) 2016; 11 Felzenszwalb (10.1016/j.rse.2019.111322_bib20) 2004; 59 Zhao (10.1016/j.rse.2019.111322_bib102) 2016; 54 Tuia (10.1016/j.rse.2019.111322_bib80) 2010; 7 Zhao (10.1016/j.rse.2019.111322_bib104) 2015; 36 Li (10.1016/j.rse.2019.111322_bib43) 2017; 10 Zhong (10.1016/j.rse.2019.111322_bib106) 2014; 52 Burnett (10.1016/j.rse.2019.111322_bib11) 2003; 168 Zhang (10.1016/j.rse.2019.111322_bib100) 2006; 44 Mnih (10.1016/j.rse.2019.111322_bib56) 2013 Chen (10.1016/j.rse.2019.111322_bib14) 2018; 40 Demir (10.1016/j.rse.2019.111322_bib15) 2012; 50 Myint (10.1016/j.rse.2019.111322_bib58) 2011; 115 Marmanis (10.1016/j.rse.2019.111322_bib52) 2016; 13 Xia (10.1016/j.rse.2019.111322_bib86) 2017; 26 Xue (10.1016/j.rse.2019.111322_bib91) 2007; 8 Persello (10.1016/j.rse.2019.111322_bib69) 2017; 14 Fauvel (10.1016/j.rse.2019.111322_bib19) 2013; 101 Zhao (10.1016/j.rse.2019.111322_bib103) 2016; 113 Lee (10.1016/j.rse.2019.111322_bib42) 2013; vol. 3 Hu (10.1016/j.rse.2019.111322_bib28) 2015; 7 Ma (10.1016/j.rse.2019.111322_bib47) 2017; 130 Pacifici (10.1016/j.rse.2019.111322_bib64) 2009; 113 Ma (10.1016/j.rse.2019.111322_bib48) 2017; 130 Izquierdo-Verdiguier (10.1016/j.rse.2019.111322_bib34) 2013; 10 Yang (10.1016/j.rse.2019.111322_bib93) 2016; 9 Ozdarici-Ok (10.1016/j.rse.2019.111322_bib63) 2015; 7 Bruzzone (10.1016/j.rse.2019.111322_bib9) 2006; 44 Olofsson (10.1016/j.rse.2019.111322_bib61) 2014; 148 Giada (10.1016/j.rse.2019.111322_bib23) 2003; 24 LeCun (10.1016/j.rse.2019.111322_bib41) 2015; 521 Badrinarayanan (10.1016/j.rse.2019.111322_bib3) 2017; 39 Maggiori (10.1016/j.rse.2019.111322_bib50) 2017 Matasci (10.1016/j.rse.2019.111322_bib53) 2015; 53 Gong (10.1016/j.rse.2019.111322_bib25) 1992; 40 Sherrah (10.1016/j.rse.2019.111322_bib73) 2016 Demir (10.1016/j.rse.2019.111322_bib16) 2014; 52 Othman (10.1016/j.rse.2019.111322_bib62) 2017; 55 Sheng (10.1016/j.rse.2019.111322_bib72) 2016; 185 Lu (10.1016/j.rse.2019.111322_bib45) 2016; 13 Yu (10.1016/j.rse.2019.111322_bib95) 2016; 8 Volpi (10.1016/j.rse.2019.111322_bib82) 2017; 55 Mattyus (10.1016/j.rse.2019.111322_bib55) 2015 Zhong (10.1016/j.rse.2019.111322_bib105) 2017; 9 Bruzzone (10.1016/j.rse.2019.111322_bib10) 2009; 47 Kussul (10.1016/j.rse.2019.111322_bib39) 2017; 14 Napoletano (10.1016/j.rse.2019.111322_bib59) 2018; 39 Ojala (10.1016/j.rse.2019.111322_bib60) 2002; 24 Hu (10.1016/j.rse.2019.111322_bib29) 2016; 8 Zhang (10.1016/j.rse.2019.111322_bib99) 2018; 216 Hu (10.1016/j.rse.2019.111322_bib31) 2015; 7 Hu (10.1016/j.rse.2019.111322_bib30) 2017 Persello (10.1016/j.rse.2019.111322_bib68) 2014; 52 Vuolo (10.1016/j.rse.2019.111322_bib83) 2018; 72 Zeiler (10.1016/j.rse.2019.111322_bib96) 2014 Jiang (10.1016/j.rse.2019.111322_bib36) 2017; 32 Mathieu (10.1016/j.rse.2019.111322_bib54) 2007; 81 Kussul (10.1016/j.rse.2019.111322_bib40) 2015; 40 Benediktsson (10.1016/j.rse.2019.111322_bib4) 2005; 43 Wu (10.1016/j.rse.2019.111322_bib84) 2006; 1 Zhang (10.1016/j.rse.2019.111322_bib97) 2012; 13 Tarabalka (10.1016/j.rse.2019.111322_bib75) 2010; 40 Xia (10.1016/j.rse.2019.111322_bib89) 2018 Huang (10.1016/j.rse.2019.111322_bib33) 2018; 214 Jensen (10.1016/j.rse.2019.111322_bib35) 1986; 2 Maggiori (10.1016/j.rse.2019.111322_bib51) 2017; 55 Shao (10.1016/j.rse.2019.111322_bib71) 2016; 174 Shi (10.1016/j.rse.2019.111322_bib74) 2015; 12 Benz (10.1016/j.rse.2019.111322_bib5) 2004; 58 Deng (10.1016/j.rse.2019.111322_bib17) 2009 Liu (10.1016/j.rse.2019.111322_bib44) 2017; 9 Persello (10.1016/j.rse.2019.111322_bib67) 2012; 50 Zhu (10.1016/j.rse.2019.111322_bib107) 2017; 5 Xia (10.1016/j.rse.2019.111322_bib85) 2010; 88 Paisitkriangkrai (10.1016/j.rse.2019.111322_bib65) 2015 Tarabalka (10.1016/j.rse.2019.111322_bib76) 2010; 7 Paisitkriangkrai (10.1016/j.rse.2019.111322_bib66) 2016; 9 Xia (10.1016/j.rse.2019.111322_bib87) 2017 Tong (10.1016/j.rse.2019.111322_bib77) 2018 Casals-Carrasco (10.1016/j.rse.2019.111322_bib12) 2000; 21 Shao (10.1016/j.rse.2019.111322_bib70) 2013; 34 Zhang (10.1016/j.rse.2019.111322_bib98) 2018; 140 Hu (10.1016/j.rse.2019.111322_bib32) 2013; 5 Lu (10.1016/j.rse.2019.111322_bib46) 2017; 8 Xia (10.1016/j.rse.2019.111322_bib90) 2017; 55 Moser (10.1016/j.rse.2019.111322_bib57) 2013; 101 Uijlings (10.1016/j.rse.2019.111322_bib81) 2013; 104 Yan (10.1016/j.rse.2019.111322_bib92) 2006; 27 Tuia (10.1016/j.rse.2019.111322_bib79) 2016; 4 Zhao (10.1016/j.rse.2019.111322_bib101) 2017; 14 Bruzzone (10.1016/j.rse.2019.111322_bib8) 2006; 44 Yang (10.1016/j.rse.2019.111322_bib94) 2015; 53 Blaschke (10.1016/j.rse.2019.111322_bib7) 2010; 65 Haralick (10.1016/j.rse.2019.111322_bib26) 1973 Blaschke (10.1016/j.rse.2019.111322_bib6) 2001; 6 Jun (10.1016/j.rse.2019.111322_bib37) 2011; 49 Duro (10.1016/j.rse.2019.111322_bib18) 2012; 118 Gómez-Chova (10.1016/j.rse.2019.111322_bib24) 2008; 5 Gerke (10.1016/j.rse.2019.111322_bib22) 2014 Audebert (10.1016/j.rse.2019.111322_bib2) 2016 He (10.1016/j.rse.2019.111322_bib27) 2016 Chakraborty (10.1016/j.rse.2019.111322_bib13) 2015; 37 |
| References_xml | – volume: 10 start-page: 2022 year: 2017 end-page: 2035 ident: bib43 article-title: Iterative reweighting heterogeneous transfer learning framework for supervised remote sensing image classification publication-title: IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing – start-page: 1689 year: 2015 end-page: 1697 ident: bib55 article-title: Enhancing road maps by parsing aerial images around the world publication-title: IEEE International Conference on Computer Vision – volume: 12 start-page: 1948 year: 2015 end-page: 1952 ident: bib74 article-title: Accurate urban area detection in remote sensing images publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 66 start-page: 762 year: 2011 end-page: 775 ident: bib1 article-title: Markov random field-based super-resolution mapping for identification of urban trees in vhr images publication-title: ISPRS J. Photogrammetry Remote Sens. – volume: 168 start-page: 233 year: 2003 end-page: 249 ident: bib11 article-title: A multi-scale segmentation/object relationship modelling methodology for landscape analysis publication-title: Ecol. Model. – volume: 118 start-page: 259 year: 2012 end-page: 272 ident: bib18 article-title: A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using spot-5 hrg imagery publication-title: Remote Sens. Environ. – year: 2010 ident: bib88 article-title: Structural High-Resolution Satellite Image Indexing. in: ISPRS TC VII Symposium C 100 Years ISPRS, Vienna, Austria – volume: 11 year: 2016 ident: bib78 article-title: Kernel manifold alignment for domain adaptation publication-title: Public Library of Science – start-page: 1097 year: 2012 end-page: 1105 ident: bib38 article-title: Imagenet classification with deep convolutional neural networks publication-title: International Conference on Neural Information Processing Systems – volume: 148 start-page: 42 year: 2014 end-page: 57 ident: bib61 article-title: Good practices for estimating area and assessing accuracy of land change publication-title: Remote Sens. Environ. – volume: 113 start-page: 155 year: 2016 end-page: 165 ident: bib103 article-title: Learning multiscale and deep representations for classifying remotely sensed imagery publication-title: ISPRS J. Photogrammetry Remote Sens. – volume: 2 year: 1986 ident: bib35 article-title: Introductory digital image processing: a remote sensing perspective publication-title: Geocarto Int. – volume: 115 start-page: 1145 year: 2011 end-page: 1161 ident: bib58 article-title: Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery publication-title: Remote Sens. Environ. – volume: 40 start-page: 137 year: 1992 end-page: 151 ident: bib25 article-title: A comparison of spatial feature extraction algorithms for land-use classification with spot hrv data publication-title: Remote Sens. Environ. – volume: 9 start-page: 522 year: 2017 ident: bib44 article-title: Hourglass-shapenetwork based semantic segmentation for high resolution aerial imagery publication-title: Remote Sens. – volume: 65 start-page: 2 year: 2010 end-page: 16 ident: bib7 article-title: Object based image analysis for remote sensing publication-title: ISPRS J. Photogrammetry Remote Sens. – volume: 174 start-page: 258 year: 2016 end-page: 265 ident: bib71 article-title: An evaluation of time-series smoothing algorithms for land-cover classifications using modis-ndvi multi-temporal data publication-title: Remote Sens. Environ. – volume: 37 start-page: 1945 year: 2015 end-page: 1958 ident: bib13 article-title: Active batch selection via convex relaxations with guaranteed solution bounds publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 58 start-page: 239 year: 2004 end-page: 258 ident: bib5 article-title: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for gis-ready information publication-title: ISPRS J. Photogrammetry Remote Sens. – volume: 24 start-page: 971 year: 2002 end-page: 987 ident: bib60 article-title: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 104 start-page: 154 year: 2013 end-page: 171 ident: bib81 article-title: Selective search for object recognition publication-title: Int. J. Comput. Vis. – volume: 13 start-page: 105 year: 2016 end-page: 109 ident: bib52 article-title: Deep learning earth observation classification using imagenet pretrained networks publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 5 start-page: 8 year: 2017 end-page: 36 ident: bib107 article-title: Deep learning in remote sensing: a comprehensive review and list of resources publication-title: IEEE Geoscience and Remote Sensing Magazine – volume: 49 start-page: 2662 year: 2011 end-page: 2673 ident: bib37 article-title: Spatially adaptive classification of land cover with remote sensing data publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 130 start-page: 277 year: 2017 end-page: 293 ident: bib47 article-title: A review of supervised object-based land-cover image classification publication-title: ISPRS J. Photogrammetry Remote Sens. – volume: 55 start-page: 3965 year: 2017 end-page: 3981 ident: bib90 article-title: Aid: a benchmark data set for performance evaluation of aerial scene classification publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 59 start-page: 167 year: 2004 end-page: 181 ident: bib20 article-title: Efficient graph-based image segmentation publication-title: Int. J. Comput. Vis. – volume: 101 start-page: 631 year: 2013 end-page: 651 ident: bib57 article-title: Land-cover mapping by markov modeling of spatial–contextual information in very-high-resolution remote sensing images publication-title: Proc. IEEE – volume: 44 start-page: 2587 year: 2006 end-page: 2600 ident: bib8 article-title: A multilevel context-based system for classification of very high spatial resolution images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 185 start-page: 129 year: 2016 end-page: 141 ident: bib72 article-title: Representative lake water extent mapping at continental scales using multi-temporal landsat-8 imagery publication-title: Remote Sens. Environ. – volume: 4 start-page: 41 year: 2016 end-page: 57 ident: bib79 article-title: Domain adaptation for the classification of remote sensing data: an overview of recent advances publication-title: IEEE Geoscience and Remote Sensing Magazine – volume: 43 start-page: 480 year: 2005 end-page: 491 ident: bib4 article-title: Classification of hyperspectral data from urban areas based on extended morphological profiles publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 7 start-page: 14988 year: 2015 end-page: 15013 ident: bib31 article-title: A comparative study of sampling analysis in the scene classification of optical high-spatial resolution remote sensing imagery publication-title: Remote Sens. – volume: 9 start-page: 1030 year: 2017 ident: bib105 article-title: Scene semantic understanding based on the spatial context relations of multiple objects publication-title: Remote Sens. – volume: 24 start-page: 4251 year: 2003 end-page: 4266 ident: bib23 article-title: Information extraction from very high resolution satellite imagery over lukole refugee camp, Tanzania publication-title: Int. J. Remote Sens. – volume: 14 start-page: 1436 year: 2017 end-page: 1440 ident: bib101 article-title: Transfer learning with fully pretrained deep convolution networks for land-use classification publication-title: IEEE Geosci. Remote Sens. Lett. – year: 2017 ident: bib87 article-title: Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation. CoRR Abs/1707 – volume: 7 start-page: 5611 year: 2015 end-page: 5638 ident: bib63 article-title: Mapping of agricultural crops from single high-resolution multispectral imagesdata-driven smoothing vs. parcel-based smoothing publication-title: Remote Sens. – volume: 52 start-page: 7023 year: 2014 end-page: 7037 ident: bib106 article-title: A hybrid object-oriented conditional random field classification framework for high spatial resolution remote sensing imagery publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 39 start-page: 2481 year: 2017 end-page: 2495 ident: bib3 article-title: Segnet: a deep convolutional encoder-decoder architecture for image segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 39 start-page: 1343 year: 2018 end-page: 1376 ident: bib59 article-title: Visual descriptors for content-based retrieval of remote-sensing images publication-title: Int. J. Remote Sens. – volume: 7 start-page: 14680 year: 2015 end-page: 14707 ident: bib28 article-title: Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery publication-title: Remote Sens. – volume: 113 start-page: 1276 year: 2009 end-page: 1292 ident: bib64 article-title: A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification publication-title: Remote Sens. Environ. – volume: 14 start-page: 778 year: 2017 end-page: 782 ident: bib39 article-title: Deep learning classification of land cover and crop types using remote sensing data publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 5 start-page: 336 year: 2008 end-page: 340 ident: bib24 article-title: Semisupervised image classification with laplacian support vector machines publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 50 start-page: 1930 year: 2012 end-page: 1941 ident: bib15 article-title: Detection of land-cover transitions in multitemporal remote sensing images with active-learning-based compound classification publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 32 start-page: 726 year: 2017 end-page: 737 ident: bib36 article-title: Retrieving aerial scene images with learned deep image-sketch features publication-title: J. Comput. Sci. Technol. – volume: 21 start-page: 3039 year: 2000 end-page: 3055 ident: bib12 article-title: Application of spectral mixture analysis for terrain evaluation studies publication-title: Int. J. Remote Sens. – volume: 34 start-page: 8588 year: 2013 end-page: 8602 ident: bib70 article-title: Extreme value theory-based calibration for the fusion of multiple features in high-resolution satellite scene classification publication-title: Int. J. Remote Sens. – start-page: 770 year: 2016 end-page: 778 ident: bib27 article-title: Deep residual learning for image recognition publication-title: IEEE Conference on Computer Vision and Pattern Recognition – volume: 130 start-page: 277 year: 2017 end-page: 293 ident: bib48 article-title: A review of supervised object-based land-cover image classification publication-title: ISPRS J. Photogrammetry Remote Sens. – volume: 81 start-page: 179 year: 2007 end-page: 192 ident: bib54 article-title: Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery publication-title: Landsc. Urban Plan. – year: 2018 ident: bib77 article-title: Large-scale Land Cover Classification in Gaofen-2 Satellite Imagery – start-page: 192 year: 2017 end-page: 197 ident: bib30 article-title: Deep sparse representations for land-use scene classification in remote sensing images publication-title: IEEE International Conference on Signal Processing – volume: 54 start-page: 2108 year: 2016 end-page: 2123 ident: bib102 article-title: Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 14 start-page: 2325 year: 2017 end-page: 2329 ident: bib69 article-title: Deep fully convolutional networks for the detection of informal settlements in vhr images publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 53 start-page: 3550 year: 2015 end-page: 3564 ident: bib53 article-title: Semisupervised transfer component analysis for domain adaptation in remote sensing image classification publication-title: IEEE Trans. Geosci. Remote Sens. – volume: vol. 6 year: 2017 ident: bib21 article-title: Borrowing treasures from the wealthy: deep transfer learning through selective joint fine-tuning publication-title: IEEE Conference on Computer Vision and Pattern Recognition – volume: 6 start-page: 12 year: 2001 end-page: 17 ident: bib6 article-title: What's wrong with pixels? some recent developments interfacing remote sensing and gis publication-title: GeoBIT/GIS – volume: 9 start-page: 543 year: 2016 end-page: 555 ident: bib93 article-title: Domain adaptation with preservation of manifold geometry for hyperspectral image classification publication-title: IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing – volume: vol. 3 start-page: 2 year: 2013 ident: bib42 article-title: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks publication-title: Workshop on Challenges in Representation Learning, ICML – volume: 88 start-page: 382 year: 2010 end-page: 403 ident: bib85 article-title: Shape-based invariant texture indexing publication-title: Int. J. Comput. Vis. – volume: 53 start-page: 4472 year: 2015 end-page: 4482 ident: bib94 article-title: Learning high-level features for satellite image classification with limited labeled samples publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 1 start-page: 10 year: 2006 end-page: 16 ident: bib84 article-title: Fuzzy svm for content-based image retrieval: a pseudo-label support vector machine framework publication-title: IEEE Comput. Intell. Mag. – volume: 8 start-page: 259 year: 2016 ident: bib95 article-title: A color-texture-structure descriptor for high-resolution satellite image classification publication-title: Remote Sens. – volume: 521 start-page: 436 year: 2015 ident: bib41 article-title: Deep learning publication-title: Nature – volume: 13 start-page: 693 year: 2012 end-page: 712 ident: bib97 article-title: The application of small unmanned aerial systems for precision agriculture: a review publication-title: Precis. Agric. – volume: 5 start-page: 6026 year: 2013 end-page: 6042 ident: bib32 article-title: Exploring the use of google earth imagery and object-based methods in land use/cover mapping publication-title: Remote Sens. – volume: 140 start-page: 133 year: 2018 end-page: 144 ident: bib98 article-title: A hybrid mlp-cnn classifier for very fine resolution remotely sensed image classification publication-title: ISPRS J. Photogrammetry Remote Sens. – start-page: 5091 year: 2016 end-page: 5094 ident: bib2 article-title: How useful is region-based classification of remote sensing images in a deep learning framework? publication-title: IEEE International Geoscience and Remote Sensing Symposium – start-page: 818 year: 2014 end-page: 833 ident: bib96 article-title: Visualizing and understanding convolutional networks publication-title: European Conference on Computer Vision – start-page: 36 year: 2015 end-page: 43 ident: bib65 article-title: Effective semantic pixel labelling with convolutional networks and conditional random fields publication-title: IEEE Conference on Computer Vision and Pattern Recognition Workshops – year: 2013 ident: bib56 article-title: Machine Learning for Aerial Image Labeling – volume: 27 start-page: 4039 year: 2006 end-page: 4055 ident: bib92 article-title: Comparison of pixel-based and object-oriented image classification approachesa case study in a coal fire area, wuda, inner Mongolia, China publication-title: Int. J. Remote Sens. – volume: 101 start-page: 652 year: 2013 end-page: 675 ident: bib19 article-title: Advances in spectral-spatial classification of hyperspectral images publication-title: Proc. IEEE – year: 2016 ident: bib73 article-title: Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery – volume: 40 start-page: 1267 year: 2010 end-page: 1279 ident: bib75 article-title: Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers publication-title: IEEE Trans. on Systems, Man, and Cybernetics, Part B (Cybernetics) – volume: 55 start-page: 645 year: 2017 end-page: 657 ident: bib51 article-title: Convolutional neural networks for large-scale remote-sensing image classification publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 8 start-page: 555 year: 2016 ident: bib29 article-title: Fast binary coding for the scene classification of high-resolution remote sensing imagery publication-title: Remote Sens. – start-page: 248 year: 2009 end-page: 255 ident: bib17 article-title: Imagenet: a large-scale hierarchical image database publication-title: IEEE Conference on Computer Vision and Pattern Recognition – volume: 8 start-page: 35 year: 2007 end-page: 63 ident: bib91 article-title: Multi-task learning for classification with dirichlet process priors publication-title: J. Mach. Learn. Res. – volume: 52 start-page: 6937 year: 2014 end-page: 6956 ident: bib68 article-title: Active and semisupervised learning for the classification of remote sensing images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 10 start-page: 981 year: 2013 end-page: 985 ident: bib34 article-title: Encoding invariances in remote sensing image classification with svm publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 40 start-page: 45 year: 2015 ident: bib40 article-title: Regional scale crop mapping using multi-temporal satellite imagery publication-title: Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. – year: 2018 ident: bib89 article-title: Dota: a large-scale dataset for object detection in aerial images publication-title: IEEE Conference on Computer Vision and Pattern Recognition – start-page: 610 year: 1973 end-page: 621 ident: bib26 article-title: Textural features for image classification publication-title: IEEE Trans. on Systems, Man, and Cybernetics – volume: 47 start-page: 3180 year: 2009 end-page: 3191 ident: bib10 article-title: A novel approach to the selection of spatially invariant features for the classification of hyperspectral images with improved generalization capability publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 9 start-page: 2868 year: 2016 end-page: 2881 ident: bib66 article-title: Semantic labeling of aerial and satellite imagery publication-title: IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing – volume: 52 start-page: 1272 year: 2014 end-page: 1284 ident: bib16 article-title: Definition of effective training sets for supervised classification of remote sensing images by a novel cost-sensitive active learning method publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 8 start-page: 1210 year: 2017 end-page: 1219 ident: bib46 article-title: Active learning for training sample selection in remote sensing image classification using spatial information publication-title: Remote Sensing Letters – volume: 26 start-page: 5005 year: 2017 end-page: 5018 ident: bib86 article-title: Texture characterization using shape co-occurrence patterns publication-title: IEEE Trans. Image Process. – volume: 72 start-page: 122 year: 2018 end-page: 130 ident: bib83 article-title: How much does multi-temporal sentinel-2 data improve crop type classification? publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 7 start-page: 736 year: 2010 end-page: 740 ident: bib76 article-title: Svm-and mrf-based method for accurate classification of hyperspectral images publication-title: IEEE Geosci. Remote Sens. Lett. – year: 2014 ident: bib22 article-title: Isprs semantic labeling contest publication-title: Photogrammetric Computer Vision – volume: 55 start-page: 4441 year: 2017 end-page: 4456 ident: bib62 article-title: Domain adaptation network for cross-scene classification publication-title: IEEE Trans. Geosci. Remote Sens. – year: 2016 ident: bib49 article-title: High-resolution Semantic Labeling with Convolutional Neural Networks – volume: 214 start-page: 73 year: 2018 end-page: 86 ident: bib33 article-title: Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery publication-title: Remote Sens. Environ. – volume: 55 start-page: 881 year: 2017 end-page: 893 ident: bib82 article-title: Dense semantic labeling of subdecimeter resolution images with convolutional neural networks publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 216 start-page: 57 year: 2018 end-page: 70 ident: bib99 article-title: An object-based convolutional neural network (ocnn) for urban land use classification publication-title: Remote Sens. Environ. – volume: 44 start-page: 3363 year: 2006 end-page: 3373 ident: bib9 article-title: A novel transductive svm for semisupervised classification of remote-sensing images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 40 start-page: 834 year: 2018 end-page: 848 ident: bib14 article-title: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 13 start-page: 515 year: 2016 end-page: 519 ident: bib45 article-title: A novel mrf-based multifeature fusion for classification of remote sensing images publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 44 start-page: 2950 year: 2006 end-page: 2961 ident: bib100 article-title: A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery publication-title: IEEE Trans. Geosci. Remote Sens. – start-page: 3226 year: 2017 end-page: 3229 ident: bib50 article-title: Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark publication-title: IEEE International Symposium on Geoscience and Remote Sensing – volume: 50 start-page: 4468 year: 2012 end-page: 4483 ident: bib67 article-title: Active learning for domain adaptation in the supervised classification of remote sensing images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 7 start-page: 88 year: 2010 end-page: 92 ident: bib80 article-title: Multisource composite kernels for urban-image classification publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 36 start-page: 3368 year: 2015 end-page: 3379 ident: bib104 article-title: On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery publication-title: Int. J. Remote Sens. – volume: 50 start-page: 1930 issue: 5 year: 2012 ident: 10.1016/j.rse.2019.111322_bib15 article-title: Detection of land-cover transitions in multitemporal remote sensing images with active-learning-based compound classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2011.2168534 – volume: 10 start-page: 981 issue: 5 year: 2013 ident: 10.1016/j.rse.2019.111322_bib34 article-title: Encoding invariances in remote sensing image classification with svm publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2012.2227297 – volume: 113 start-page: 155 year: 2016 ident: 10.1016/j.rse.2019.111322_bib103 article-title: Learning multiscale and deep representations for classifying remotely sensed imagery publication-title: ISPRS J. Photogrammetry Remote Sens. doi: 10.1016/j.isprsjprs.2016.01.004 – volume: 1 start-page: 10 issue: 2 year: 2006 ident: 10.1016/j.rse.2019.111322_bib84 article-title: Fuzzy svm for content-based image retrieval: a pseudo-label support vector machine framework publication-title: IEEE Comput. Intell. Mag. doi: 10.1109/MCI.2006.1626490 – year: 2014 ident: 10.1016/j.rse.2019.111322_bib22 article-title: Isprs semantic labeling contest – volume: 24 start-page: 971 issue: 7 year: 2002 ident: 10.1016/j.rse.2019.111322_bib60 article-title: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2002.1017623 – start-page: 1689 year: 2015 ident: 10.1016/j.rse.2019.111322_bib55 article-title: Enhancing road maps by parsing aerial images around the world – volume: 101 start-page: 631 issue: 3 year: 2013 ident: 10.1016/j.rse.2019.111322_bib57 article-title: Land-cover mapping by markov modeling of spatial–contextual information in very-high-resolution remote sensing images publication-title: Proc. IEEE doi: 10.1109/JPROC.2012.2211551 – volume: 11 issue: 2 year: 2016 ident: 10.1016/j.rse.2019.111322_bib78 article-title: Kernel manifold alignment for domain adaptation publication-title: Public Library of Science – volume: 65 start-page: 2 issue: 1 year: 2010 ident: 10.1016/j.rse.2019.111322_bib7 article-title: Object based image analysis for remote sensing publication-title: ISPRS J. Photogrammetry Remote Sens. doi: 10.1016/j.isprsjprs.2009.06.004 – volume: 7 start-page: 5611 issue: 5 year: 2015 ident: 10.1016/j.rse.2019.111322_bib63 article-title: Mapping of agricultural crops from single high-resolution multispectral imagesdata-driven smoothing vs. parcel-based smoothing publication-title: Remote Sens. doi: 10.3390/rs70505611 – volume: 5 start-page: 8 issue: 4 year: 2017 ident: 10.1016/j.rse.2019.111322_bib107 article-title: Deep learning in remote sensing: a comprehensive review and list of resources publication-title: IEEE Geoscience and Remote Sensing Magazine doi: 10.1109/MGRS.2017.2762307 – volume: 54 start-page: 2108 issue: 4 year: 2016 ident: 10.1016/j.rse.2019.111322_bib102 article-title: Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2015.2496185 – volume: 88 start-page: 382 issue: 3 year: 2010 ident: 10.1016/j.rse.2019.111322_bib85 article-title: Shape-based invariant texture indexing publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-009-0312-3 – volume: 8 start-page: 555 issue: 7 year: 2016 ident: 10.1016/j.rse.2019.111322_bib29 article-title: Fast binary coding for the scene classification of high-resolution remote sensing imagery publication-title: Remote Sens. doi: 10.3390/rs8070555 – volume: 53 start-page: 3550 issue: 7 year: 2015 ident: 10.1016/j.rse.2019.111322_bib53 article-title: Semisupervised transfer component analysis for domain adaptation in remote sensing image classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2014.2377785 – volume: 58 start-page: 239 issue: 3–4 year: 2004 ident: 10.1016/j.rse.2019.111322_bib5 article-title: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for gis-ready information publication-title: ISPRS J. Photogrammetry Remote Sens. doi: 10.1016/j.isprsjprs.2003.10.002 – volume: 12 start-page: 1948 issue: 9 year: 2015 ident: 10.1016/j.rse.2019.111322_bib74 article-title: Accurate urban area detection in remote sensing images publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2015.2439696 – year: 2016 ident: 10.1016/j.rse.2019.111322_bib49 – volume: 113 start-page: 1276 issue: 6 year: 2009 ident: 10.1016/j.rse.2019.111322_bib64 article-title: A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2009.02.014 – volume: 39 start-page: 1343 issue: 5 year: 2018 ident: 10.1016/j.rse.2019.111322_bib59 article-title: Visual descriptors for content-based retrieval of remote-sensing images publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2017.1399472 – volume: 216 start-page: 57 year: 2018 ident: 10.1016/j.rse.2019.111322_bib99 article-title: An object-based convolutional neural network (ocnn) for urban land use classification publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.06.034 – volume: 9 start-page: 1030 issue: 10 year: 2017 ident: 10.1016/j.rse.2019.111322_bib105 article-title: Scene semantic understanding based on the spatial context relations of multiple objects publication-title: Remote Sens. doi: 10.3390/rs9101030 – volume: 5 start-page: 6026 issue: 11 year: 2013 ident: 10.1016/j.rse.2019.111322_bib32 article-title: Exploring the use of google earth imagery and object-based methods in land use/cover mapping publication-title: Remote Sens. doi: 10.3390/rs5116026 – volume: 2 issue: 1 year: 1986 ident: 10.1016/j.rse.2019.111322_bib35 article-title: Introductory digital image processing: a remote sensing perspective publication-title: Geocarto Int. doi: 10.1080/10106048709354084 – volume: 8 start-page: 1210 issue: 12 year: 2017 ident: 10.1016/j.rse.2019.111322_bib46 article-title: Active learning for training sample selection in remote sensing image classification using spatial information publication-title: Remote Sensing Letters doi: 10.1080/2150704X.2017.1375610 – start-page: 610 issue: 6 year: 1973 ident: 10.1016/j.rse.2019.111322_bib26 article-title: Textural features for image classification publication-title: IEEE Trans. on Systems, Man, and Cybernetics doi: 10.1109/TSMC.1973.4309314 – year: 2018 ident: 10.1016/j.rse.2019.111322_bib89 article-title: Dota: a large-scale dataset for object detection in aerial images – volume: 34 start-page: 8588 issue: 23 year: 2013 ident: 10.1016/j.rse.2019.111322_bib70 article-title: Extreme value theory-based calibration for the fusion of multiple features in high-resolution satellite scene classification publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2013.845925 – volume: 148 start-page: 42 year: 2014 ident: 10.1016/j.rse.2019.111322_bib61 article-title: Good practices for estimating area and assessing accuracy of land change publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2014.02.015 – start-page: 770 year: 2016 ident: 10.1016/j.rse.2019.111322_bib27 article-title: Deep residual learning for image recognition – start-page: 1097 year: 2012 ident: 10.1016/j.rse.2019.111322_bib38 article-title: Imagenet classification with deep convolutional neural networks – volume: 9 start-page: 2868 issue: 7 year: 2016 ident: 10.1016/j.rse.2019.111322_bib66 article-title: Semantic labeling of aerial and satellite imagery publication-title: IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing doi: 10.1109/JSTARS.2016.2582921 – volume: 40 start-page: 137 issue: 2 year: 1992 ident: 10.1016/j.rse.2019.111322_bib25 article-title: A comparison of spatial feature extraction algorithms for land-use classification with spot hrv data publication-title: Remote Sens. Environ. doi: 10.1016/0034-4257(92)90011-8 – volume: 55 start-page: 645 issue: 2 year: 2017 ident: 10.1016/j.rse.2019.111322_bib51 article-title: Convolutional neural networks for large-scale remote-sensing image classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2016.2612821 – volume: 40 start-page: 45 issue: 7 year: 2015 ident: 10.1016/j.rse.2019.111322_bib40 article-title: Regional scale crop mapping using multi-temporal satellite imagery publication-title: Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. doi: 10.5194/isprsarchives-XL-7-W3-45-2015 – year: 2017 ident: 10.1016/j.rse.2019.111322_bib87 – volume: 40 start-page: 1267 issue: 5 year: 2010 ident: 10.1016/j.rse.2019.111322_bib75 article-title: Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers publication-title: IEEE Trans. on Systems, Man, and Cybernetics, Part B (Cybernetics) doi: 10.1109/TSMCB.2009.2037132 – volume: 130 start-page: 277 year: 2017 ident: 10.1016/j.rse.2019.111322_bib48 article-title: A review of supervised object-based land-cover image classification publication-title: ISPRS J. Photogrammetry Remote Sens. doi: 10.1016/j.isprsjprs.2017.06.001 – volume: 14 start-page: 1436 issue: 9 year: 2017 ident: 10.1016/j.rse.2019.111322_bib101 article-title: Transfer learning with fully pretrained deep convolution networks for land-use classification publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2017.2691013 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 10.1016/j.rse.2019.111322_bib41 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 214 start-page: 73 year: 2018 ident: 10.1016/j.rse.2019.111322_bib33 article-title: Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.04.050 – volume: 14 start-page: 2325 issue: 12 year: 2017 ident: 10.1016/j.rse.2019.111322_bib69 article-title: Deep fully convolutional networks for the detection of informal settlements in vhr images publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2017.2763738 – volume: 10 start-page: 2022 issue: 5 year: 2017 ident: 10.1016/j.rse.2019.111322_bib43 article-title: Iterative reweighting heterogeneous transfer learning framework for supervised remote sensing image classification publication-title: IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing doi: 10.1109/JSTARS.2016.2646138 – volume: 27 start-page: 4039 issue: 18 year: 2006 ident: 10.1016/j.rse.2019.111322_bib92 article-title: Comparison of pixel-based and object-oriented image classification approachesa case study in a coal fire area, wuda, inner Mongolia, China publication-title: Int. J. Remote Sens. doi: 10.1080/01431160600702632 – year: 2016 ident: 10.1016/j.rse.2019.111322_bib73 – volume: 37 start-page: 1945 issue: 10 year: 2015 ident: 10.1016/j.rse.2019.111322_bib13 article-title: Active batch selection via convex relaxations with guaranteed solution bounds publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2015.2389848 – volume: 72 start-page: 122 year: 2018 ident: 10.1016/j.rse.2019.111322_bib83 article-title: How much does multi-temporal sentinel-2 data improve crop type classification? publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 174 start-page: 258 year: 2016 ident: 10.1016/j.rse.2019.111322_bib71 article-title: An evaluation of time-series smoothing algorithms for land-cover classifications using modis-ndvi multi-temporal data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2015.12.023 – volume: 21 start-page: 3039 issue: 16 year: 2000 ident: 10.1016/j.rse.2019.111322_bib12 article-title: Application of spectral mixture analysis for terrain evaluation studies publication-title: Int. J. Remote Sens. doi: 10.1080/01431160050144947 – volume: 6 start-page: 12 year: 2001 ident: 10.1016/j.rse.2019.111322_bib6 article-title: What's wrong with pixels? some recent developments interfacing remote sensing and gis publication-title: GeoBIT/GIS – volume: 9 start-page: 543 issue: 2 year: 2016 ident: 10.1016/j.rse.2019.111322_bib93 article-title: Domain adaptation with preservation of manifold geometry for hyperspectral image classification publication-title: IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing doi: 10.1109/JSTARS.2015.2449738 – year: 2018 ident: 10.1016/j.rse.2019.111322_bib77 – volume: 5 start-page: 336 issue: 3 year: 2008 ident: 10.1016/j.rse.2019.111322_bib24 article-title: Semisupervised image classification with laplacian support vector machines publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2008.916070 – volume: 101 start-page: 652 issue: 3 year: 2013 ident: 10.1016/j.rse.2019.111322_bib19 article-title: Advances in spectral-spatial classification of hyperspectral images publication-title: Proc. IEEE doi: 10.1109/JPROC.2012.2197589 – volume: 104 start-page: 154 issue: 2 year: 2013 ident: 10.1016/j.rse.2019.111322_bib81 article-title: Selective search for object recognition publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-013-0620-5 – volume: 130 start-page: 277 year: 2017 ident: 10.1016/j.rse.2019.111322_bib47 article-title: A review of supervised object-based land-cover image classification publication-title: ISPRS J. Photogrammetry Remote Sens. doi: 10.1016/j.isprsjprs.2017.06.001 – volume: 40 start-page: 834 issue: 4 year: 2018 ident: 10.1016/j.rse.2019.111322_bib14 article-title: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2017.2699184 – volume: 8 start-page: 259 issue: 3 year: 2016 ident: 10.1016/j.rse.2019.111322_bib95 article-title: A color-texture-structure descriptor for high-resolution satellite image classification publication-title: Remote Sens. doi: 10.3390/rs8030259 – volume: 32 start-page: 726 issue: 4 year: 2017 ident: 10.1016/j.rse.2019.111322_bib36 article-title: Retrieving aerial scene images with learned deep image-sketch features publication-title: J. Comput. Sci. Technol. doi: 10.1007/s11390-017-1754-7 – start-page: 192 year: 2017 ident: 10.1016/j.rse.2019.111322_bib30 article-title: Deep sparse representations for land-use scene classification in remote sensing images – volume: 49 start-page: 2662 issue: 7 year: 2011 ident: 10.1016/j.rse.2019.111322_bib37 article-title: Spatially adaptive classification of land cover with remote sensing data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2011.2105490 – volume: 81 start-page: 179 issue: 3 year: 2007 ident: 10.1016/j.rse.2019.111322_bib54 article-title: Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery publication-title: Landsc. Urban Plan. doi: 10.1016/j.landurbplan.2006.11.009 – volume: 7 start-page: 88 issue: 1 year: 2010 ident: 10.1016/j.rse.2019.111322_bib80 article-title: Multisource composite kernels for urban-image classification publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2009.2015341 – volume: 24 start-page: 4251 issue: 22 year: 2003 ident: 10.1016/j.rse.2019.111322_bib23 article-title: Information extraction from very high resolution satellite imagery over lukole refugee camp, Tanzania publication-title: Int. J. Remote Sens. doi: 10.1080/0143116021000035021 – volume: 4 start-page: 41 issue: 2 year: 2016 ident: 10.1016/j.rse.2019.111322_bib79 article-title: Domain adaptation for the classification of remote sensing data: an overview of recent advances publication-title: IEEE Geoscience and Remote Sensing Magazine doi: 10.1109/MGRS.2016.2548504 – volume: 8 start-page: 35 issue: Jan year: 2007 ident: 10.1016/j.rse.2019.111322_bib91 article-title: Multi-task learning for classification with dirichlet process priors publication-title: J. Mach. Learn. Res. – start-page: 3226 year: 2017 ident: 10.1016/j.rse.2019.111322_bib50 article-title: Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark – year: 2010 ident: 10.1016/j.rse.2019.111322_bib88 – volume: 185 start-page: 129 year: 2016 ident: 10.1016/j.rse.2019.111322_bib72 article-title: Representative lake water extent mapping at continental scales using multi-temporal landsat-8 imagery publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2015.12.041 – volume: 36 start-page: 3368 issue: 13 year: 2015 ident: 10.1016/j.rse.2019.111322_bib104 article-title: On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery publication-title: Int. J. Remote Sens. doi: 10.1080/2150704X.2015.1062157 – start-page: 818 year: 2014 ident: 10.1016/j.rse.2019.111322_bib96 article-title: Visualizing and understanding convolutional networks – volume: 53 start-page: 4472 issue: 8 year: 2015 ident: 10.1016/j.rse.2019.111322_bib94 article-title: Learning high-level features for satellite image classification with limited labeled samples publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2015.2400449 – volume: 55 start-page: 4441 issue: 8 year: 2017 ident: 10.1016/j.rse.2019.111322_bib62 article-title: Domain adaptation network for cross-scene classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2017.2692281 – volume: 13 start-page: 105 issue: 1 year: 2016 ident: 10.1016/j.rse.2019.111322_bib52 article-title: Deep learning earth observation classification using imagenet pretrained networks publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2015.2499239 – start-page: 36 year: 2015 ident: 10.1016/j.rse.2019.111322_bib65 article-title: Effective semantic pixel labelling with convolutional networks and conditional random fields – volume: 13 start-page: 693 issue: 6 year: 2012 ident: 10.1016/j.rse.2019.111322_bib97 article-title: The application of small unmanned aerial systems for precision agriculture: a review publication-title: Precis. Agric. doi: 10.1007/s11119-012-9274-5 – volume: 50 start-page: 4468 issue: 11 year: 2012 ident: 10.1016/j.rse.2019.111322_bib67 article-title: Active learning for domain adaptation in the supervised classification of remote sensing images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2012.2192740 – volume: 7 start-page: 14988 issue: 11 year: 2015 ident: 10.1016/j.rse.2019.111322_bib31 article-title: A comparative study of sampling analysis in the scene classification of optical high-spatial resolution remote sensing imagery publication-title: Remote Sens. doi: 10.3390/rs71114988 – volume: 140 start-page: 133 year: 2018 ident: 10.1016/j.rse.2019.111322_bib98 article-title: A hybrid mlp-cnn classifier for very fine resolution remotely sensed image classification publication-title: ISPRS J. Photogrammetry Remote Sens. doi: 10.1016/j.isprsjprs.2017.07.014 – volume: 118 start-page: 259 year: 2012 ident: 10.1016/j.rse.2019.111322_bib18 article-title: A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using spot-5 hrg imagery publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2011.11.020 – volume: 26 start-page: 5005 issue: 10 year: 2017 ident: 10.1016/j.rse.2019.111322_bib86 article-title: Texture characterization using shape co-occurrence patterns publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2726182 – volume: 52 start-page: 6937 issue: 11 year: 2014 ident: 10.1016/j.rse.2019.111322_bib68 article-title: Active and semisupervised learning for the classification of remote sensing images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2014.2305805 – volume: 55 start-page: 3965 issue: 7 year: 2017 ident: 10.1016/j.rse.2019.111322_bib90 article-title: Aid: a benchmark data set for performance evaluation of aerial scene classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2017.2685945 – volume: 52 start-page: 7023 issue: 11 year: 2014 ident: 10.1016/j.rse.2019.111322_bib106 article-title: A hybrid object-oriented conditional random field classification framework for high spatial resolution remote sensing imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2014.2306692 – volume: 39 start-page: 2481 issue: 12 year: 2017 ident: 10.1016/j.rse.2019.111322_bib3 article-title: Segnet: a deep convolutional encoder-decoder architecture for image segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2016.2644615 – volume: 44 start-page: 3363 issue: 11 year: 2006 ident: 10.1016/j.rse.2019.111322_bib9 article-title: A novel transductive svm for semisupervised classification of remote-sensing images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2006.877950 – volume: 14 start-page: 778 issue: 5 year: 2017 ident: 10.1016/j.rse.2019.111322_bib39 article-title: Deep learning classification of land cover and crop types using remote sensing data publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2017.2681128 – volume: 7 start-page: 14680 issue: 11 year: 2015 ident: 10.1016/j.rse.2019.111322_bib28 article-title: Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery publication-title: Remote Sens. doi: 10.3390/rs71114680 – volume: vol. 6 year: 2017 ident: 10.1016/j.rse.2019.111322_bib21 article-title: Borrowing treasures from the wealthy: deep transfer learning through selective joint fine-tuning – volume: 44 start-page: 2587 issue: 9 year: 2006 ident: 10.1016/j.rse.2019.111322_bib8 article-title: A multilevel context-based system for classification of very high spatial resolution images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2006.875360 – volume: 13 start-page: 515 issue: 4 year: 2016 ident: 10.1016/j.rse.2019.111322_bib45 article-title: A novel mrf-based multifeature fusion for classification of remote sensing images publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2016.2521418 – volume: 52 start-page: 1272 issue: 2 year: 2014 ident: 10.1016/j.rse.2019.111322_bib16 article-title: Definition of effective training sets for supervised classification of remote sensing images by a novel cost-sensitive active learning method publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2013.2249522 – year: 2013 ident: 10.1016/j.rse.2019.111322_bib56 – volume: 66 start-page: 762 issue: 6 year: 2011 ident: 10.1016/j.rse.2019.111322_bib1 article-title: Markov random field-based super-resolution mapping for identification of urban trees in vhr images publication-title: ISPRS J. Photogrammetry Remote Sens. doi: 10.1016/j.isprsjprs.2011.08.002 – volume: 55 start-page: 881 issue: 2 year: 2017 ident: 10.1016/j.rse.2019.111322_bib82 article-title: Dense semantic labeling of subdecimeter resolution images with convolutional neural networks publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2016.2616585 – volume: 7 start-page: 736 issue: 4 year: 2010 ident: 10.1016/j.rse.2019.111322_bib76 article-title: Svm-and mrf-based method for accurate classification of hyperspectral images publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2010.2047711 – volume: 59 start-page: 167 issue: 2 year: 2004 ident: 10.1016/j.rse.2019.111322_bib20 article-title: Efficient graph-based image segmentation publication-title: Int. J. Comput. Vis. doi: 10.1023/B:VISI.0000022288.19776.77 – volume: 43 start-page: 480 issue: 3 year: 2005 ident: 10.1016/j.rse.2019.111322_bib4 article-title: Classification of hyperspectral data from urban areas based on extended morphological profiles publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2004.842478 – start-page: 5091 year: 2016 ident: 10.1016/j.rse.2019.111322_bib2 article-title: How useful is region-based classification of remote sensing images in a deep learning framework? – volume: 47 start-page: 3180 issue: 9 year: 2009 ident: 10.1016/j.rse.2019.111322_bib10 article-title: A novel approach to the selection of spatially invariant features for the classification of hyperspectral images with improved generalization capability publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2009.2019636 – volume: 115 start-page: 1145 issue: 5 year: 2011 ident: 10.1016/j.rse.2019.111322_bib58 article-title: Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2010.12.017 – start-page: 248 year: 2009 ident: 10.1016/j.rse.2019.111322_bib17 article-title: Imagenet: a large-scale hierarchical image database – volume: vol. 3 start-page: 2 year: 2013 ident: 10.1016/j.rse.2019.111322_bib42 article-title: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks – volume: 168 start-page: 233 issue: 3 year: 2003 ident: 10.1016/j.rse.2019.111322_bib11 article-title: A multi-scale segmentation/object relationship modelling methodology for landscape analysis publication-title: Ecol. Model. doi: 10.1016/S0304-3800(03)00139-X – volume: 44 start-page: 2950 issue: 10 year: 2006 ident: 10.1016/j.rse.2019.111322_bib100 article-title: A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2006.876704 – volume: 9 start-page: 522 issue: 6 year: 2017 ident: 10.1016/j.rse.2019.111322_bib44 article-title: Hourglass-shapenetwork based semantic segmentation for high resolution aerial imagery publication-title: Remote Sens. doi: 10.3390/rs9060522 |
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| SubjectTerms | Artificial neural networks Classification Datasets Deep learning Detection Gaofen-2 satellite images High resolution High-resolution remote sensing Image acquisition Image classification Image resolution Image segmentation Internet Labels Land cover land-cover classification Mapping Neural networks Remote sensing Satellite imagery Spatial data Spatial resolution Target recognition |
| Title | Land-cover classification with high-resolution remote sensing images using transferable deep models |
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