A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images

Change detection in high resolution remote sensing images is crucial to the understanding of land surface changes. As traditional change detection methods are not suitable for the task considering the challenges brought by the fine image details and complex texture features conveyed in high resoluti...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	ISPRS journal of photogrammetry and remote sensing Ročník 166; s. 183 - 200
Hlavní autoři:	Zhang, Chenxiao, Yue, Peng, Tapete, Deodato, Jiang, Liangcun, Shangguan, Boyi, Huang, Li, Liu, Guangchao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.08.2020
Témata:	Change detection China cities data collection Deep supervision network High resolution remote sensing image Image difference discrimination Image fusion Internet remote sensing texture China Deep supervision network Change detection High resolution remote sensing image Image difference discrimination Image fusion
ISSN:	0924-2716, 1872-8235
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Change detection in high resolution remote sensing images is crucial to the understanding of land surface changes. As traditional change detection methods are not suitable for the task considering the challenges brought by the fine image details and complex texture features conveyed in high resolution images, a number of deep learning-based change detection methods have been proposed to improve the change detection performance. Although the state-of-the-art deep feature based methods outperform all the other deep learning-based change detection methods, networks in the existing deep feature based methods are mostly modified from architectures that are originally proposed for single-image semantic segmentation. Transferring these networks for change detection task still poses some key issues. In this paper, we propose a deeply supervised image fusion network (IFN) for change detection in high resolution bi-temporal remote sensing images. Specifically, highly representative deep features of bi-temporal images are firstly extracted through a fully convolutional two-stream architecture. Then, the extracted deep features are fed into a deeply supervised difference discrimination network (DDN) for change detection. To improve boundary completeness and internal compactness of objects in the output change maps, multi-level deep features of raw images are fused with image difference features by means of attention modules for change map reconstruction. DDN is further enhanced by directly introducing change map losses to intermediate layers in the network, and the whole network is trained in an end-to-end manner. IFN is applied to a publicly available dataset, as well as a challenging dataset consisting of multi-source bi-temporal images from Google Earth covering different cities in China. Both visual interpretation and quantitative assessment confirm that IFN outperforms four benchmark methods derived from the literature, by returning changed areas with complete boundaries and high internal compactness compared to the state-of-the-art methods.
AbstractList	Change detection in high resolution remote sensing images is crucial to the understanding of land surface changes. As traditional change detection methods are not suitable for the task considering the challenges brought by the fine image details and complex texture features conveyed in high resolution images, a number of deep learning-based change detection methods have been proposed to improve the change detection performance. Although the state-of-the-art deep feature based methods outperform all the other deep learning-based change detection methods, networks in the existing deep feature based methods are mostly modified from architectures that are originally proposed for single-image semantic segmentation. Transferring these networks for change detection task still poses some key issues. In this paper, we propose a deeply supervised image fusion network (IFN) for change detection in high resolution bi-temporal remote sensing images. Specifically, highly representative deep features of bi-temporal images are firstly extracted through a fully convolutional two-stream architecture. Then, the extracted deep features are fed into a deeply supervised difference discrimination network (DDN) for change detection. To improve boundary completeness and internal compactness of objects in the output change maps, multi-level deep features of raw images are fused with image difference features by means of attention modules for change map reconstruction. DDN is further enhanced by directly introducing change map losses to intermediate layers in the network, and the whole network is trained in an end-to-end manner. IFN is applied to a publicly available dataset, as well as a challenging dataset consisting of multi-source bi-temporal images from Google Earth covering different cities in China. Both visual interpretation and quantitative assessment confirm that IFN outperforms four benchmark methods derived from the literature, by returning changed areas with complete boundaries and high internal compactness compared to the state-of-the-art methods.
Author	Liu, Guangchao Zhang, Chenxiao Jiang, Liangcun Huang, Li Shangguan, Boyi Yue, Peng Tapete, Deodato
Author_xml	– sequence: 1 givenname: Chenxiao surname: Zhang fullname: Zhang, Chenxiao organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China – sequence: 2 givenname: Peng orcidid: 0000-0003-3006-4542 surname: Yue fullname: Yue, Peng email: pyue@whu.edu.cn organization: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China – sequence: 3 givenname: Deodato orcidid: 0000-0002-7242-4473 surname: Tapete fullname: Tapete, Deodato organization: Italian Space Agency (ASI), Via del Politecnico snc, 00133, Rome, Italy – sequence: 4 givenname: Liangcun surname: Jiang fullname: Jiang, Liangcun organization: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China – sequence: 5 givenname: Boyi surname: Shangguan fullname: Shangguan, Boyi organization: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China – sequence: 6 givenname: Li surname: Huang fullname: Huang, Li organization: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China – sequence: 7 givenname: Guangchao surname: Liu fullname: Liu, Guangchao organization: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China
BookMark	eNqNkE1v1DAQhi1UJLaF34CPXJKOP-LEBw6rClqkSr3A2XKcya63WTvYSVH_PV624tALHEYjzfuh0XNJLkIMSMhHBjUDpq4Ptc9zyocyNQcONagaQLwhG9a1vOq4aC7IBjSXFW-Zekcucz4AAGtUtyFpSwfEeXqmeZ0xPfmMA_VHu0M6rtnHQAMuv2J6pGNM1O1tKMqAC7rlJPpA9363pwlznNY_p95XCx7nmOxUzse4IM0Ysg-7c29-T96Odsr44WVfkR9fv3y_uavuH26_3WzvKyeZXCo9WqF7iQJ4r0CPzqlGCKY77lC2KEZl-6HpnHPIW9n3o1IoZA9SNRqEluKKfDr3zin-XDEv5uizw2myAeOaDdedAtkI3RZre7a6FHNOOJo5lWfTs2FgTpTNwfylbE6UDShTKJfk51dJ5xd7ArEk66f_yG_PeSwknjwmk53H4HDwqTA2Q_T_7PgNDbykNw
CitedBy_id	crossref_primary_10_3390_rs15184609 crossref_primary_10_1109_TGRS_2024_3484178 crossref_primary_10_1109_TGRS_2022_3169479 crossref_primary_10_1109_TGRS_2025_3608816 crossref_primary_10_1016_j_jhydrol_2023_129455 crossref_primary_10_1109_JSTARS_2024_3379565 crossref_primary_10_1109_JSTARS_2025_3597194 crossref_primary_10_1016_j_isprsjprs_2025_07_034 crossref_primary_10_1109_JSTARS_2024_3402431 crossref_primary_10_1109_ACCESS_2022_3160163 crossref_primary_10_1109_TGRS_2021_3139077 crossref_primary_10_1109_JSTARS_2025_3555536 crossref_primary_10_3390_rs16081372 crossref_primary_10_1016_j_isprsjprs_2022_08_012 crossref_primary_10_3390_rs17132249 crossref_primary_10_1109_TGRS_2025_3550973 crossref_primary_10_1117_1_JRS_18_044510 crossref_primary_10_3390_rs14030722 crossref_primary_10_1109_TGRS_2023_3300533 crossref_primary_10_3390_rs13081440 crossref_primary_10_1016_j_isprsjprs_2023_11_004 crossref_primary_10_1080_22797254_2022_2047795 crossref_primary_10_1109_LGRS_2021_3050746 crossref_primary_10_1109_JSTARS_2024_3435575 crossref_primary_10_1109_JSTARS_2025_3601996 crossref_primary_10_1109_JSTARS_2025_3528185 crossref_primary_10_1109_JSTARS_2025_3567508 crossref_primary_10_1155_2022_3404858 crossref_primary_10_1109_TGRS_2024_3471075 crossref_primary_10_1109_TETCI_2025_3529893 crossref_primary_10_1155_2023_4200153 crossref_primary_10_1109_TGRS_2024_3365825 crossref_primary_10_1109_JSTARS_2023_3342989 crossref_primary_10_1109_JSTARS_2024_3409072 crossref_primary_10_1109_TGRS_2025_3536473 crossref_primary_10_1016_j_isprsjprs_2025_06_022 crossref_primary_10_1109_TGRS_2024_3410977 crossref_primary_10_3390_app13106167 crossref_primary_10_1109_TGRS_2024_3419790 crossref_primary_10_1016_j_engappai_2024_108960 crossref_primary_10_1109_TGRS_2021_3095166 crossref_primary_10_3390_rs15081958 crossref_primary_10_3390_su14031319 crossref_primary_10_1016_j_isprsjprs_2023_11_023 crossref_primary_10_1109_TGRS_2025_3566097 crossref_primary_10_1109_JSTARS_2025_3554659 crossref_primary_10_1007_s00530_023_01063_4 crossref_primary_10_1109_TGRS_2023_3278739 crossref_primary_10_1109_TGRS_2024_3470808 crossref_primary_10_1016_j_patcog_2024_111266 crossref_primary_10_1007_s10489_023_04535_2 crossref_primary_10_1080_10095020_2025_2508224 crossref_primary_10_1080_15481603_2023_2257980 crossref_primary_10_1007_s40808_021_01258_6 crossref_primary_10_1109_JSEN_2025_3543717 crossref_primary_10_3390_rs13244971 crossref_primary_10_1109_TIM_2021_3096872 crossref_primary_10_3390_rs15030588 crossref_primary_10_1109_JSTARS_2024_3522910 crossref_primary_10_1109_JSTARS_2023_3327340 crossref_primary_10_1109_TGRS_2023_3286113 crossref_primary_10_3390_rs17172970 crossref_primary_10_1016_j_cageo_2025_105880 crossref_primary_10_3390_rs16050844 crossref_primary_10_1109_TGRS_2021_3066802 crossref_primary_10_1109_TGRS_2025_3591087 crossref_primary_10_3390_rs14040957 crossref_primary_10_3390_rs13234927 crossref_primary_10_1109_JSTARS_2023_3330753 crossref_primary_10_1109_TGRS_2024_3386934 crossref_primary_10_3390_app12157903 crossref_primary_10_1109_TGRS_2024_3432771 crossref_primary_10_1016_j_engappai_2023_106666 crossref_primary_10_1109_TGRS_2022_3168126 crossref_primary_10_1109_LGRS_2023_3319648 crossref_primary_10_3390_rs13183670 crossref_primary_10_1109_TGRS_2021_3134691 crossref_primary_10_3390_rs17172949 crossref_primary_10_3390_rs14246352 crossref_primary_10_3390_land12030681 crossref_primary_10_1109_JSTARS_2022_3177235 crossref_primary_10_1109_TGRS_2024_3432320 crossref_primary_10_1109_ACCESS_2020_3041759 crossref_primary_10_3390_rs15235459 crossref_primary_10_1080_01431161_2024_2398225 crossref_primary_10_1109_JSTARS_2024_3400458 crossref_primary_10_1109_TGRS_2024_3371463 crossref_primary_10_1109_JSTARS_2022_3200997 crossref_primary_10_1016_j_rse_2022_113371 crossref_primary_10_1109_TGRS_2024_3382116 crossref_primary_10_1109_JSTARS_2025_3559085 crossref_primary_10_1109_TGRS_2023_3241257 crossref_primary_10_1109_TGRS_2024_3491715 crossref_primary_10_1109_LGRS_2024_3461957 crossref_primary_10_1109_TGRS_2024_3502768 crossref_primary_10_1109_LGRS_2024_3359220 crossref_primary_10_1016_j_jag_2022_102676 crossref_primary_10_1109_TGRS_2021_3131993 crossref_primary_10_1109_JSTARS_2024_3413715 crossref_primary_10_1109_TGRS_2024_3361847 crossref_primary_10_1109_JSTARS_2024_3485687 crossref_primary_10_1109_TGRS_2024_3360516 crossref_primary_10_1007_s11263_024_02141_4 crossref_primary_10_1109_JSTARS_2024_3422687 crossref_primary_10_1109_TGRS_2023_3299642 crossref_primary_10_1109_TGRS_2024_3370556 crossref_primary_10_1109_TGRS_2025_3560724 crossref_primary_10_3390_rs16071169 crossref_primary_10_1109_TGRS_2022_3154390 crossref_primary_10_3390_rs14246361 crossref_primary_10_3390_app15168794 crossref_primary_10_1080_10095020_2022_2162980 crossref_primary_10_1109_TGRS_2024_3495216 crossref_primary_10_3390_rs15051232 crossref_primary_10_1016_j_isprsjprs_2023_05_011 crossref_primary_10_1109_JSTARS_2023_3278726 crossref_primary_10_1109_JSTARS_2025_3584959 crossref_primary_10_5772_geet_17 crossref_primary_10_3390_rs16224186 crossref_primary_10_1016_j_isprsjprs_2023_04_001 crossref_primary_10_1016_j_isprsjprs_2025_08_023 crossref_primary_10_3390_ijgi11040263 crossref_primary_10_1016_j_engappai_2025_111573 crossref_primary_10_1109_TGRS_2022_3168331 crossref_primary_10_1155_2023_4448861 crossref_primary_10_1109_TGRS_2023_3261273 crossref_primary_10_1007_s11220_022_00404_3 crossref_primary_10_1117_1_JRS_16_046509 crossref_primary_10_1109_LGRS_2021_3049370 crossref_primary_10_1007_s12145_024_01315_5 crossref_primary_10_1007_s41651_024_00202_3 crossref_primary_10_1016_j_isprsjprs_2023_05_033 crossref_primary_10_1109_TGRS_2024_3386918 crossref_primary_10_3390_rs14143464 crossref_primary_10_1109_ACCESS_2023_3292531 crossref_primary_10_1109_TGRS_2024_3476992 crossref_primary_10_1109_JSTARS_2025_3570108 crossref_primary_10_1109_JSTARS_2024_3432200 crossref_primary_10_1109_TGRS_2023_3271024 crossref_primary_10_1109_TIM_2025_3608333 crossref_primary_10_1016_j_jag_2021_102597 crossref_primary_10_1117_1_JRS_19_016515 crossref_primary_10_1109_LGRS_2023_3305623 crossref_primary_10_1109_LGRS_2023_3303200 crossref_primary_10_3390_rs14143297 crossref_primary_10_3390_rs14215379 crossref_primary_10_1016_j_jag_2021_102582 crossref_primary_10_1080_22797254_2022_2161419 crossref_primary_10_1109_JSTARS_2024_3384545 crossref_primary_10_1109_JSTARS_2025_3597817 crossref_primary_10_1016_j_jag_2021_102348 crossref_primary_10_1109_TGRS_2024_3486787 crossref_primary_10_3390_app15179407 crossref_primary_10_1080_01431161_2024_2343139 crossref_primary_10_1109_TGRS_2023_3345645 crossref_primary_10_1109_TGRS_2025_3556237 crossref_primary_10_1109_JSTARS_2025_3584758 crossref_primary_10_1109_JSTARS_2025_3601739 crossref_primary_10_1109_TGRS_2023_3241436 crossref_primary_10_1109_TGRS_2023_3334521 crossref_primary_10_1109_JSTARS_2025_3539674 crossref_primary_10_3390_su142013167 crossref_primary_10_3390_rs14215368 crossref_primary_10_3390_rs17101749 crossref_primary_10_1109_TGRS_2025_3560977 crossref_primary_10_1109_TGRS_2023_3337816 crossref_primary_10_1109_JSTARS_2024_3522066 crossref_primary_10_1109_TGRS_2023_3325829 crossref_primary_10_1016_j_isprsjprs_2023_06_017 crossref_primary_10_1109_LGRS_2024_3419155 crossref_primary_10_1109_LGRS_2024_3516775 crossref_primary_10_1109_TGRS_2024_3362914 crossref_primary_10_3390_rs15225268 crossref_primary_10_1109_TGRS_2023_3313619 crossref_primary_10_3390_rs14215598 crossref_primary_10_1109_TPAMI_2023_3237896 crossref_primary_10_1007_s00521_022_08122_3 crossref_primary_10_1080_01431161_2022_2131479 crossref_primary_10_1109_LGRS_2021_3103991 crossref_primary_10_1109_TGRS_2023_3344583 crossref_primary_10_1080_22797254_2023_2196641 crossref_primary_10_1007_s11042_021_11779_y crossref_primary_10_1109_JSTARS_2025_3539416 crossref_primary_10_1117_1_JRS_19_016504 crossref_primary_10_1016_j_rsase_2024_101167 crossref_primary_10_1109_JSTARS_2024_3400925 crossref_primary_10_1109_TGRS_2024_3410389 crossref_primary_10_1109_TGRS_2025_3546808 crossref_primary_10_1016_j_eswa_2024_123731 crossref_primary_10_1109_JSTARS_2025_3526785 crossref_primary_10_3390_rs13224528 crossref_primary_10_3390_rs15082145 crossref_primary_10_1016_j_ijdrr_2025_105664 crossref_primary_10_1080_2150704X_2024_2424503 crossref_primary_10_3390_rs15061682 crossref_primary_10_1109_JSTARS_2023_3241157 crossref_primary_10_1016_j_imavis_2024_105157 crossref_primary_10_1088_1742_6596_1961_1_012028 crossref_primary_10_1109_TGRS_2025_3584092 crossref_primary_10_1016_j_imavis_2024_105150 crossref_primary_10_1109_TGRS_2025_3584094 crossref_primary_10_3390_rs15153799 crossref_primary_10_1109_JSTARS_2024_3358298 crossref_primary_10_1109_JSTARS_2024_3415171 crossref_primary_10_1109_TGRS_2023_3305554 crossref_primary_10_3390_rs15010087 crossref_primary_10_1109_JSTARS_2023_3337999 crossref_primary_10_1109_JSTARS_2024_3490754 crossref_primary_10_1080_13682199_2024_2410673 crossref_primary_10_1109_JSTARS_2023_3348630 crossref_primary_10_1016_j_isprsjprs_2024_09_024 crossref_primary_10_1109_ACCESS_2025_3572928 crossref_primary_10_3390_rs14215448 crossref_primary_10_3390_rs16101736 crossref_primary_10_1109_ACCESS_2025_3559992 crossref_primary_10_1109_TGRS_2023_3327139 crossref_primary_10_1109_TGRS_2024_3413677 crossref_primary_10_1016_j_isprsjprs_2024_09_027 crossref_primary_10_1109_JSTARS_2024_3487137 crossref_primary_10_3390_s25051345 crossref_primary_10_1007_s12046_023_02262_5 crossref_primary_10_1109_TGRS_2023_3272694 crossref_primary_10_1109_TGRS_2025_3544402 crossref_primary_10_1109_TGRS_2024_3357524 crossref_primary_10_1016_j_jclepro_2021_127640 crossref_primary_10_3390_rs15245670 crossref_primary_10_1016_j_isprsjprs_2022_05_001 crossref_primary_10_1007_s13042_023_01880_z crossref_primary_10_1080_01431161_2022_2161856 crossref_primary_10_1109_JSEN_2025_3583301 crossref_primary_10_1016_j_jag_2022_103110 crossref_primary_10_1016_j_isprsjprs_2025_03_008 crossref_primary_10_1016_j_engappai_2024_108163 crossref_primary_10_1109_JSTARS_2021_3113831 crossref_primary_10_1109_JSTARS_2024_3373201 crossref_primary_10_1109_TGRS_2024_3427848 crossref_primary_10_1109_LGRS_2025_3551754 crossref_primary_10_1109_TGRS_2024_3383800 crossref_primary_10_1109_JSTARS_2024_3354944 crossref_primary_10_1109_LGRS_2023_3323367 crossref_primary_10_1016_j_isprsjprs_2024_09_002 crossref_primary_10_1109_JSTARS_2023_3298097 crossref_primary_10_1109_TPAMI_2025_3578494 crossref_primary_10_1016_j_isprsjprs_2021_10_015 crossref_primary_10_3390_rs15174285 crossref_primary_10_1109_JSTARS_2024_3511597 crossref_primary_10_1109_TGRS_2024_3500036 crossref_primary_10_1117_1_JRS_17_016515 crossref_primary_10_3390_rs15092395 crossref_primary_10_1109_JSTARS_2022_3204541 crossref_primary_10_3390_rs16203852 crossref_primary_10_1109_TGRS_2025_3592731 crossref_primary_10_1016_j_isprsjprs_2021_10_001 crossref_primary_10_1109_ACCESS_2022_3161978 crossref_primary_10_1109_JSTARS_2025_3567287 crossref_primary_10_3390_rs15040949 crossref_primary_10_1109_TGRS_2022_3171067 crossref_primary_10_1007_s12524_025_02265_1 crossref_primary_10_1109_LGRS_2023_3270799 crossref_primary_10_1038_s41598_024_63257_8 crossref_primary_10_1109_JSTARS_2023_3310208 crossref_primary_10_1007_s11227_025_07344_1 crossref_primary_10_1109_ACCESS_2022_3201129 crossref_primary_10_3390_rs14215402 crossref_primary_10_1109_TGRS_2024_3441944 crossref_primary_10_1109_TGRS_2024_3523128 crossref_primary_10_1117_1_JRS_19_024512 crossref_primary_10_1109_JSTARS_2024_3438246 crossref_primary_10_1109_TGRS_2023_3344751 crossref_primary_10_3390_rs15143517 crossref_primary_10_1109_LGRS_2021_3110302 crossref_primary_10_3390_s20236735 crossref_primary_10_1016_j_knosys_2023_110281 crossref_primary_10_1109_JSTARS_2024_3372386 crossref_primary_10_1109_LGRS_2023_3347765 crossref_primary_10_1109_LGRS_2024_3366981 crossref_primary_10_1109_JSTARS_2024_3422901 crossref_primary_10_1109_JSTARS_2024_3439340 crossref_primary_10_3390_sym14061138 crossref_primary_10_1109_TGRS_2022_3204834 crossref_primary_10_3390_rs14194895 crossref_primary_10_1109_TGRS_2024_3367778 crossref_primary_10_1016_j_engappai_2024_109213 crossref_primary_10_1109_TGRS_2025_3532468 crossref_primary_10_1016_j_isprsjprs_2025_02_004 crossref_primary_10_1109_TGRS_2024_3352050 crossref_primary_10_1109_TGRS_2023_3321637 crossref_primary_10_1016_j_jhydrol_2023_130601 crossref_primary_10_1016_j_jag_2024_103767 crossref_primary_10_3390_rs16234569 crossref_primary_10_1007_s11263_024_02247_9 crossref_primary_10_1016_j_jag_2024_103761 crossref_primary_10_1016_j_isprsjprs_2025_05_020 crossref_primary_10_1016_j_isprsjprs_2025_05_021 crossref_primary_10_1109_LGRS_2023_3323534 crossref_primary_10_1016_j_isprsjprs_2024_11_017 crossref_primary_10_1016_j_isprsjprs_2024_11_019 crossref_primary_10_1109_TGRS_2024_3367948 crossref_primary_10_3390_rs15092351 crossref_primary_10_1109_TGRS_2024_3458951 crossref_primary_10_1007_s10661_024_12598_y crossref_primary_10_1109_JPROC_2022_3219376 crossref_primary_10_1109_TGRS_2023_3344083 crossref_primary_10_1080_01431161_2025_2457128 crossref_primary_10_1109_TPAMI_2025_3557581 crossref_primary_10_1080_01431161_2023_2243021 crossref_primary_10_1109_TGRS_2023_3314452 crossref_primary_10_1109_JSTARS_2025_3534583 crossref_primary_10_54097_mj2fea78 crossref_primary_10_1080_10106049_2021_2022013 crossref_primary_10_3390_rs14184478 crossref_primary_10_3390_rs16020266 crossref_primary_10_1109_LGRS_2025_3604651 crossref_primary_10_1109_TGRS_2023_3324025 crossref_primary_10_1109_JSTARS_2022_3176858 crossref_primary_10_1109_TGRS_2022_3165851 crossref_primary_10_1109_TGRS_2023_3348479 crossref_primary_10_3390_rs17020217 crossref_primary_10_1109_LGRS_2022_3188636 crossref_primary_10_1016_j_jag_2024_103785 crossref_primary_10_1109_JSTARS_2023_3280589 crossref_primary_10_1109_JSTARS_2023_3267137 crossref_primary_10_1109_JSTARS_2024_3373039 crossref_primary_10_1109_JSTARS_2024_3481424 crossref_primary_10_3390_rs15163972 crossref_primary_10_1007_s11432_022_3691_4 crossref_primary_10_1016_j_jag_2023_103258 crossref_primary_10_1155_2022_2189176 crossref_primary_10_1109_JSTARS_2024_3354310 crossref_primary_10_1109_TGRS_2024_3376673 crossref_primary_10_1109_TGRS_2023_3327780 crossref_primary_10_1016_j_jag_2023_103256 crossref_primary_10_3390_rs16183533 crossref_primary_10_3390_rs15040928 crossref_primary_10_1109_TGRS_2023_3348459 crossref_primary_10_3390_rs15184488 crossref_primary_10_1109_JSTARS_2024_3407972 crossref_primary_10_1109_TGRS_2025_3600397 crossref_primary_10_1016_j_isprsjprs_2024_10_010 crossref_primary_10_1109_JSTARS_2024_3414452 crossref_primary_10_1109_JSTARS_2023_3344635 crossref_primary_10_1109_JSTARS_2024_3439510 crossref_primary_10_3390_rs15153740 crossref_primary_10_1016_j_isprsjprs_2025_04_023 crossref_primary_10_1007_s13042_024_02284_3 crossref_primary_10_1109_TGRS_2025_3527009 crossref_primary_10_1109_TGRS_2023_3277496 crossref_primary_10_1109_TIP_2024_3349868 crossref_primary_10_1109_TGRS_2022_3227098 crossref_primary_10_1109_TGRS_2025_3555959 crossref_primary_10_1109_TGRS_2025_3545906 crossref_primary_10_1016_j_isprsjprs_2024_10_021 crossref_primary_10_1109_JSTARS_2021_3129318 crossref_primary_10_3390_rs15010045 crossref_primary_10_1109_JSTARS_2023_3317488 crossref_primary_10_1007_s00530_024_01623_2 crossref_primary_10_1109_JSTARS_2024_3405971 crossref_primary_10_3390_rs14122801 crossref_primary_10_1109_TGRS_2022_3213925 crossref_primary_10_1080_01431161_2023_2257860 crossref_primary_10_1109_JSTARS_2023_3344633 crossref_primary_10_3390_rs15071860 crossref_primary_10_1109_TGRS_2023_3245674 crossref_primary_10_1016_j_jag_2022_103141 crossref_primary_10_1109_JSTARS_2024_3452948 crossref_primary_10_1109_TGRS_2024_3365990 crossref_primary_10_1109_TVLSI_2024_3438728 crossref_primary_10_1109_TGRS_2024_3424532 crossref_primary_10_1016_j_knosys_2025_113994 crossref_primary_10_3390_electronics12010035 crossref_primary_10_1016_j_engappai_2024_108774 crossref_primary_10_1016_j_isprsjprs_2025_04_008 crossref_primary_10_1109_TCSVT_2024_3494820 crossref_primary_10_1109_JSTARS_2024_3482559 crossref_primary_10_1109_TGRS_2024_3433014 crossref_primary_10_1109_TGRS_2025_3583166 crossref_primary_10_1007_s42965_023_00318_5 crossref_primary_10_14358_PERS_23_00069R2 crossref_primary_10_1007_s12665_022_10713_1 crossref_primary_10_1109_JSTARS_2025_3531499 crossref_primary_10_1109_TGRS_2024_3369059 crossref_primary_10_1109_JSTARS_2025_3541260 crossref_primary_10_1109_TGRS_2024_3362895 crossref_primary_10_1109_LGRS_2025_3555584 crossref_primary_10_3390_rs14122834 crossref_primary_10_1109_TGRS_2023_3326813 crossref_primary_10_1007_s40808_024_02068_2 crossref_primary_10_3390_rs15102645 crossref_primary_10_1109_LGRS_2022_3144304 crossref_primary_10_1109_JSTARS_2023_3260006 crossref_primary_10_1109_LGRS_2024_3507760 crossref_primary_10_1109_TGRS_2025_3589090 crossref_primary_10_1109_TGRS_2024_3357085 crossref_primary_10_1016_j_neucom_2025_130607 crossref_primary_10_1109_JSTARS_2025_3530146 crossref_primary_10_1109_TGRS_2023_3275140 crossref_primary_10_1109_TGRS_2023_3320288 crossref_primary_10_3390_rs17132121 crossref_primary_10_1080_01431161_2021_2022241 crossref_primary_10_1109_TGRS_2023_3235917 crossref_primary_10_1109_JSTARS_2024_3461988 crossref_primary_10_1109_TGRS_2025_3535496 crossref_primary_10_3390_ijgi10090591 crossref_primary_10_1080_10095020_2024_2448232 crossref_primary_10_1109_TGRS_2024_3374421 crossref_primary_10_1109_TGRS_2024_3362680 crossref_primary_10_1109_JSTARS_2025_3577004 crossref_primary_10_1016_j_jag_2024_104008 crossref_primary_10_3390_rs14153548 crossref_primary_10_1016_j_isprsjprs_2024_04_013 crossref_primary_10_1109_JSTARS_2025_3556723 crossref_primary_10_1016_j_jag_2022_102940 crossref_primary_10_1109_TGRS_2024_3440001 crossref_primary_10_1109_JSTARS_2025_3528053 crossref_primary_10_1109_TGRS_2025_3548562 crossref_primary_10_1109_JSTARS_2025_3532783 crossref_primary_10_1080_01431161_2024_2334778 crossref_primary_10_3390_rs16173222 crossref_primary_10_1109_TGRS_2025_3545051 crossref_primary_10_1109_TGRS_2025_3540864 crossref_primary_10_1109_JSTARS_2025_3553206 crossref_primary_10_1109_LGRS_2024_3432077 crossref_primary_10_1109_TIP_2022_3226418 crossref_primary_10_1109_JSTARS_2025_3531658 crossref_primary_10_1109_JSTARS_2025_3601214 crossref_primary_10_1109_JSTARS_2023_3328315 crossref_primary_10_3390_rs14246213 crossref_primary_10_3390_rs17111849 crossref_primary_10_3390_rs14122874 crossref_primary_10_3390_s22124626 crossref_primary_10_1016_j_knosys_2025_113691 crossref_primary_10_1109_TGRS_2024_3402391 crossref_primary_10_3390_rs14092046 crossref_primary_10_3390_s25010103 crossref_primary_10_1007_s12145_024_01523_z crossref_primary_10_3390_rs17111840 crossref_primary_10_3390_rs14040841 crossref_primary_10_1109_JSTARS_2023_3314133 crossref_primary_10_1080_01431161_2025_2496533 crossref_primary_10_1109_JSTARS_2022_3217082 crossref_primary_10_1109_TGRS_2023_3344284 crossref_primary_10_1016_j_jag_2021_102615 crossref_primary_10_1109_TGRS_2025_3597745 crossref_primary_10_1016_j_displa_2024_102840 crossref_primary_10_3390_rs13234802 crossref_primary_10_1109_TGRS_2023_3325536 crossref_primary_10_3390_rs17030369 crossref_primary_10_1109_LGRS_2022_3193502 crossref_primary_10_1109_TGRS_2025_3551504 crossref_primary_10_1109_ACCESS_2025_3560591 crossref_primary_10_3390_rs14225768 crossref_primary_10_1109_TGRS_2024_3360447 crossref_primary_10_1016_j_neucom_2024_128080 crossref_primary_10_1109_JSTARS_2025_3555849 crossref_primary_10_1109_TGRS_2022_3194732 crossref_primary_10_1109_JSTARS_2025_3589266 crossref_primary_10_1109_MGRS_2024_3412770 crossref_primary_10_3390_app15041904 crossref_primary_10_1109_JSTARS_2024_3387093 crossref_primary_10_1016_j_engappai_2025_111015 crossref_primary_10_1109_JSTARS_2024_3455261 crossref_primary_10_1038_s41598_022_16329_6 crossref_primary_10_3390_rs16173278 crossref_primary_10_1080_13682199_2023_2206761 crossref_primary_10_1016_j_isprsjprs_2022_02_021 crossref_primary_10_1109_TGRS_2025_3605637 crossref_primary_10_3390_rs14071580 crossref_primary_10_3390_rs15020463 crossref_primary_10_1016_j_jag_2023_103294 crossref_primary_10_1109_TGRS_2022_3159544 crossref_primary_10_1080_01431161_2023_2224100 crossref_primary_10_1109_TGRS_2025_3542814 crossref_primary_10_1109_TGRS_2022_3209972 crossref_primary_10_1016_j_isprsjprs_2024_05_011 crossref_primary_10_1109_TAES_2025_3558725 crossref_primary_10_1109_JSTARS_2023_3335287 crossref_primary_10_1007_s12524_025_02268_y crossref_primary_10_1109_JSTARS_2023_3335281 crossref_primary_10_3788_AOS241759 crossref_primary_10_1016_j_inffus_2022_09_008 crossref_primary_10_3390_rs13030364 crossref_primary_10_1109_JSTARS_2022_3204191 crossref_primary_10_1109_JSTARS_2022_3217038 crossref_primary_10_1109_TGRS_2022_3203769 crossref_primary_10_1109_TGRS_2023_3330479 crossref_primary_10_1109_JSEN_2023_3271391 crossref_primary_10_1016_j_sigpro_2023_109203 crossref_primary_10_1109_TGRS_2024_3501303 crossref_primary_10_1109_ACCESS_2023_3235014 crossref_primary_10_3390_rs16142573 crossref_primary_10_1109_LGRS_2023_3267879 crossref_primary_10_1109_TGRS_2021_3106697 crossref_primary_10_3390_rs17050798 crossref_primary_10_1080_17538947_2024_2398051 crossref_primary_10_1109_TGRS_2024_3480091 crossref_primary_10_1080_15481603_2024_2380126 crossref_primary_10_1109_TGRS_2023_3263563 crossref_primary_10_3390_land11112028 crossref_primary_10_1109_TGRS_2021_3085870 crossref_primary_10_1080_10095020_2024_2338224 crossref_primary_10_1016_j_jag_2024_104077 crossref_primary_10_1016_j_isprsjprs_2024_06_013 crossref_primary_10_1109_TCSVT_2022_3176055 crossref_primary_10_1016_j_jag_2025_104550 crossref_primary_10_3390_rs15143566 crossref_primary_10_1109_TGRS_2025_3549818 crossref_primary_10_1109_ACCESS_2025_3575483 crossref_primary_10_1016_j_jag_2022_102769 crossref_primary_10_3390_rs13245084 crossref_primary_10_1111_tgis_12991 crossref_primary_10_1080_10106049_2024_2322080 crossref_primary_10_3390_s22030888 crossref_primary_10_1109_LGRS_2025_3562480 crossref_primary_10_3390_rs15082092 crossref_primary_10_1016_j_bspc_2024_107011 crossref_primary_10_1109_TGRS_2023_3280902 crossref_primary_10_3390_app14135415 crossref_primary_10_3390_rs14194972 crossref_primary_10_1109_JSTARS_2024_3522135 crossref_primary_10_1109_JSTARS_2024_3361507 crossref_primary_10_3390_rs16163037 crossref_primary_10_1016_j_isprsjprs_2025_01_007 crossref_primary_10_3390_rs15030694 crossref_primary_10_1038_s42256_025_01078_8 crossref_primary_10_1016_j_future_2025_107985 crossref_primary_10_1109_JSTARS_2024_3411622 crossref_primary_10_1109_LGRS_2025_3602854 crossref_primary_10_1109_TGRS_2024_3432819 crossref_primary_10_1007_s11760_023_02660_6 crossref_primary_10_1109_TIP_2024_3424335 crossref_primary_10_1109_TGRS_2022_3157721 crossref_primary_10_1111_phor_70021 crossref_primary_10_1109_TGRS_2024_3349638 crossref_primary_10_3390_rs13153053 crossref_primary_10_1016_j_image_2023_116964 crossref_primary_10_1016_j_imavis_2025_105639 crossref_primary_10_1038_s41598_025_10972_5 crossref_primary_10_1016_j_inffus_2025_103110 crossref_primary_10_1109_TGRS_2024_3405025 crossref_primary_10_3390_rs13245094 crossref_primary_10_3390_rs16193719 crossref_primary_10_1109_LGRS_2024_3452074 crossref_primary_10_3390_s24247887 crossref_primary_10_1109_JSTARS_2024_3424954 crossref_primary_10_3390_rs15225383 crossref_primary_10_3390_rs14133100 crossref_primary_10_1109_TGRS_2025_3590023 crossref_primary_10_3390_rs15082046 crossref_primary_10_1016_j_dsp_2025_105594 crossref_primary_10_1080_01431161_2024_2421935 crossref_primary_10_1109_JSTARS_2024_3373753 crossref_primary_10_1007_s12524_023_01737_6 crossref_primary_10_1016_j_asr_2025_07_023 crossref_primary_10_1109_TGRS_2023_3272006 crossref_primary_10_1109_LGRS_2024_3385404 crossref_primary_10_1038_s41598_025_15468_w crossref_primary_10_3390_rs16101765 crossref_primary_10_1109_ACCESS_2023_3307642 crossref_primary_10_1109_JSTARS_2023_3285389 crossref_primary_10_1109_JSTARS_2022_3159528 crossref_primary_10_3390_rs16071269 crossref_primary_10_1016_j_compag_2021_106491 crossref_primary_10_1109_JSTARS_2024_3434966 crossref_primary_10_1016_j_jag_2024_104282 crossref_primary_10_1109_TGRS_2025_3545012 crossref_primary_10_1080_01431161_2023_2208711 crossref_primary_10_1109_ACCESS_2024_3350173 crossref_primary_10_1016_j_isprsjprs_2024_07_001 crossref_primary_10_1109_LGRS_2024_3474181 crossref_primary_10_1080_10106049_2024_2322053 crossref_primary_10_1109_TGRS_2024_3381738 crossref_primary_10_1371_journal_pone_0239371 crossref_primary_10_3390_rs16193651 crossref_primary_10_3390_rs17010159 crossref_primary_10_1016_j_inffus_2024_102358 crossref_primary_10_1109_TGRS_2023_3268294 crossref_primary_10_1109_TGRS_2023_3272550 crossref_primary_10_1109_TGRS_2023_3297850 crossref_primary_10_1016_j_isprsjprs_2024_01_016 crossref_primary_10_1109_TGRS_2024_3468008 crossref_primary_10_1109_TGRS_2024_3403727 crossref_primary_10_1109_TNNLS_2023_3282935 crossref_primary_10_1109_TGRS_2024_3381971 crossref_primary_10_1109_TGRS_2024_3400215 crossref_primary_10_1109_JSTARS_2023_3345017 crossref_primary_10_1007_s10044_025_01420_1 crossref_primary_10_1109_JSTARS_2025_3586607 crossref_primary_10_1016_j_rse_2025_114711 crossref_primary_10_1016_j_compbiomed_2025_110346 crossref_primary_10_3390_rs16193642 crossref_primary_10_1109_TGRS_2024_3480122 crossref_primary_10_1109_TGRS_2024_3407088 crossref_primary_10_3390_rs14215577 crossref_primary_10_1007_s11356_023_25685_3 crossref_primary_10_1109_TGRS_2023_3275819 crossref_primary_10_1016_j_isprsjprs_2024_01_002 crossref_primary_10_1016_j_isprsjprs_2024_01_004 crossref_primary_10_1109_TGRS_2025_3544737 crossref_primary_10_1109_TGRS_2025_3579617 crossref_primary_10_3390_app13021037 crossref_primary_10_1109_TGRS_2024_3376384 crossref_primary_10_1038_s41598_025_94544_7 crossref_primary_10_1109_ACCESS_2024_3520428 crossref_primary_10_3390_rs16224223 crossref_primary_10_1109_TGRS_2023_3331751 crossref_primary_10_1109_TGRS_2025_3541013 crossref_primary_10_1109_TGRS_2024_3425540 crossref_primary_10_3390_rs16030572 crossref_primary_10_1016_j_asoc_2025_113372 crossref_primary_10_1109_JSTARS_2025_3525595 crossref_primary_10_1109_TGRS_2023_3327253 crossref_primary_10_1109_TGRS_2022_3196040 crossref_primary_10_3390_rs17050824 crossref_primary_10_3390_rs15030842 crossref_primary_10_1016_j_jag_2025_104393 crossref_primary_10_1109_TGRS_2024_3381751 crossref_primary_10_3390_electronics12040898 crossref_primary_10_1049_ipr2_12505 crossref_primary_10_1109_TGRS_2024_3381752 crossref_primary_10_3390_electronics12132796 crossref_primary_10_1016_j_imavis_2024_105294 crossref_primary_10_3390_rs14184527 crossref_primary_10_1109_JSTARS_2025_3576831 crossref_primary_10_1080_01431161_2023_2221797 crossref_primary_10_1109_TGRS_2022_3221492 crossref_primary_10_1109_JSTARS_2023_3326958 crossref_primary_10_1109_JSTARS_2025_3569128 crossref_primary_10_1109_TGRS_2022_3174276 crossref_primary_10_1109_JSTARS_2025_3539733 crossref_primary_10_1109_TGRS_2024_3379431 crossref_primary_10_1109_ACCESS_2025_3605984 crossref_primary_10_1145_3721135 crossref_primary_10_1016_j_jenvman_2025_124969 crossref_primary_10_1109_TGRS_2022_3176642 crossref_primary_10_3390_s25072149 crossref_primary_10_1117_1_JRS_18_016513 crossref_primary_10_1109_JSTARS_2024_3445908 crossref_primary_10_1109_MGRS_2021_3063465 crossref_primary_10_1109_TGRS_2025_3568521 crossref_primary_10_1109_JSTARS_2022_3150571 crossref_primary_10_1109_JSTARS_2025_3594028 crossref_primary_10_1109_TGRS_2021_3127580 crossref_primary_10_1111_phor_12492 crossref_primary_10_1109_JSTARS_2022_3174780 crossref_primary_10_1111_phor_12495 crossref_primary_10_1109_JSTARS_2025_3540925 crossref_primary_10_1109_JSTARS_2024_3491762 crossref_primary_10_1080_01431161_2023_2173031 crossref_primary_10_1080_01431161_2023_2173033 crossref_primary_10_3390_rs16061061 crossref_primary_10_1109_JSTARS_2025_3569104 crossref_primary_10_3390_rs16234494 crossref_primary_10_1109_TGRS_2024_3484526 crossref_primary_10_1109_JSTARS_2023_3335891 crossref_primary_10_1109_JSTARS_2025_3585568 crossref_primary_10_3390_su15043343 crossref_primary_10_1016_j_isprsjprs_2023_07_001 crossref_primary_10_3390_rs16061077 crossref_primary_10_1109_JSTARS_2024_3350044 crossref_primary_10_32604_cmc_2024_053206 crossref_primary_10_3390_rs15174141 crossref_primary_10_1016_j_isprsjprs_2022_12_009 crossref_primary_10_1109_LGRS_2021_3056416 crossref_primary_10_1109_LGRS_2025_3590959 crossref_primary_10_1109_TGRS_2022_3174009 crossref_primary_10_1016_j_asr_2023_07_069 crossref_primary_10_1109_TGRS_2024_3399215 crossref_primary_10_3390_rs15092237 crossref_primary_10_1109_JSTARS_2021_3077545 crossref_primary_10_1109_TGRS_2025_3525811 crossref_primary_10_1109_JSTARS_2024_3522329 crossref_primary_10_3390_rs13245152 crossref_primary_10_3390_rs17152575 crossref_primary_10_1109_TGRS_2025_3532349 crossref_primary_10_3390_rs14071552 crossref_primary_10_1109_TGRS_2022_3233849 crossref_primary_10_1109_TGRS_2024_3520630 crossref_primary_10_1109_TGRS_2025_3584126 crossref_primary_10_1016_j_jag_2023_103366 crossref_primary_10_1007_s11042_024_18766_z crossref_primary_10_1016_j_ipm_2021_102726 crossref_primary_10_1109_TGRS_2022_3176603 crossref_primary_10_3390_rs15092470 crossref_primary_10_3390_rs15071770 crossref_primary_10_1016_j_eswa_2023_122089 crossref_primary_10_1109_TGRS_2023_3325220 crossref_primary_10_1007_s42452_025_06814_8 crossref_primary_10_1117_1_JRS_18_034523 crossref_primary_10_1109_TGRS_2025_3584115 crossref_primary_10_3390_rs16050804 crossref_primary_10_1109_TGRS_2024_3386334 crossref_primary_10_1016_j_jhazmat_2024_133721 crossref_primary_10_1109_TGRS_2022_3228016 crossref_primary_10_1111_tgis_13133 crossref_primary_10_1109_JSTARS_2023_3306274 crossref_primary_10_3390_rs15092464 crossref_primary_10_1109_JSTARS_2024_3402388 crossref_primary_10_1016_j_isprsjprs_2021_05_002 crossref_primary_10_1109_JSTARS_2025_3544235 crossref_primary_10_1016_j_engappai_2024_108446 crossref_primary_10_1080_10106049_2024_2353253 crossref_primary_10_3390_rs15020395 crossref_primary_10_1109_TGRS_2024_3356711 crossref_primary_10_1109_TGRS_2024_3421581 crossref_primary_10_3390_electronics12234823 crossref_primary_10_3390_rs16183424 crossref_primary_10_1109_JSTARS_2023_3268104 crossref_primary_10_3390_ijgi12090382 crossref_primary_10_1109_LGRS_2022_3154745 crossref_primary_10_3390_rs14092228 crossref_primary_10_1016_j_isprsjprs_2021_05_001 crossref_primary_10_1109_TGRS_2023_3244273 crossref_primary_10_1016_j_isprsjprs_2023_09_021 crossref_primary_10_1109_JSTARS_2024_3392917 crossref_primary_10_1109_TGRS_2025_3604400 crossref_primary_10_1109_ACCESS_2022_3170704 crossref_primary_10_1016_j_rse_2021_112589 crossref_primary_10_3390_rs15092433 crossref_primary_10_1016_j_patcog_2025_112169 crossref_primary_10_1109_JSTARS_2021_3108777 crossref_primary_10_1109_TGRS_2025_3540794 crossref_primary_10_1109_TGRS_2023_3305499 crossref_primary_10_1109_JSTARS_2022_3152775 crossref_primary_10_3390_s25185832 crossref_primary_10_3390_s25092836 crossref_primary_10_1080_2150704X_2023_2264493 crossref_primary_10_1109_LGRS_2022_3165885 crossref_primary_10_1109_TGRS_2023_3235981 crossref_primary_10_1111_phor_12462 crossref_primary_10_1109_LGRS_2024_3520133 crossref_primary_10_1109_TGRS_2025_3586102 crossref_primary_10_1109_ACCESS_2024_3350641 crossref_primary_10_3390_rs16173209 crossref_primary_10_1109_JSTARS_2025_3559708 crossref_primary_10_1109_TGRS_2023_3236664 crossref_primary_10_1109_JSTARS_2024_3508692 crossref_primary_10_1109_JSTARS_2025_3591834 crossref_primary_10_1109_LGRS_2023_3324946 crossref_primary_10_1109_LGRS_2025_3574068 crossref_primary_10_1109_LGRS_2023_3341045 crossref_primary_10_1109_TGRS_2024_3493886 crossref_primary_10_1109_ACCESS_2022_3194919 crossref_primary_10_1016_j_eswa_2025_127902 crossref_primary_10_1109_JSTARS_2022_3198517 crossref_primary_10_1109_JSTARS_2023_3247455 crossref_primary_10_1109_JSTARS_2023_3264802 crossref_primary_10_3390_rs14235969 crossref_primary_10_1109_TGRS_2024_3363431 crossref_primary_10_1080_10095020_2022_2157762 crossref_primary_10_1109_TIM_2024_3387494 crossref_primary_10_1109_LGRS_2022_3216627 crossref_primary_10_3390_rs17010135 crossref_primary_10_1109_JSTARS_2024_3374290 crossref_primary_10_1109_JSTARS_2023_3245062 crossref_primary_10_3390_rs15092406 crossref_primary_10_3390_rs15184555 crossref_primary_10_3390_rs17152742 crossref_primary_10_3390_app15063061 crossref_primary_10_1109_TGRS_2024_3433373 crossref_primary_10_1109_TGRS_2024_3523097 crossref_primary_10_1109_TGRS_2024_3454055 crossref_primary_10_5194_essd_15_4749_2023 crossref_primary_10_1080_01431161_2023_2225712 crossref_primary_10_1109_TIM_2024_3373089 crossref_primary_10_1007_s40031_024_01084_1 crossref_primary_10_1109_JSTARS_2022_3203508 crossref_primary_10_3390_rs16183466 crossref_primary_10_1007_s12652_020_02722_4 crossref_primary_10_1109_LGRS_2023_3238553 crossref_primary_10_1109_ACCESS_2024_3451473 crossref_primary_10_1109_LGRS_2021_3119885 crossref_primary_10_1109_ACCESS_2025_3580481
Cites_doi	10.1109/TGRS.2019.2930348 10.1109/LGRS.2018.2817522 10.1016/j.rse.2013.01.012 10.1007/978-3-030-01234-2_1 10.1016/0034-4257(83)90010-X 10.1109/CVPRW.2009.5206848 10.1109/LGRS.2009.2025059 10.1109/LGRS.2016.2601930 10.1080/01431161.2011.648285 10.1109/ACCESS.2019.2902613 10.1007/978-3-319-24574-4_28 10.3390/rs11111382 10.1109/TGRS.2019.2916212 10.1080/01431168908903939 10.1109/LGRS.2017.2766840 10.1080/01431160500117865 10.1007/s10661-009-0798-8 10.1109/ICIVC.2017.7984667 10.1109/TNNLS.2013.2248094 10.1109/IGARSS.2019.8900173 10.1109/LGRS.2018.2889307 10.1016/j.rse.2017.07.009 10.1109/TPAMI.2016.2572683 10.1109/ICIVC.2018.8492796 10.1117/1.JRS.13.024512 10.1007/s10514-018-9734-5 10.1080/01431160801950162 10.1109/TII.2018.2873492
ContentType	Journal Article
Copyright	2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
Copyright_xml	– notice: 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
DBID	AAYXX CITATION 7S9 L.6
DOI	10.1016/j.isprsjprs.2020.06.003
DatabaseName	CrossRef AGRICOLA AGRICOLA - Academic
DatabaseTitle	CrossRef AGRICOLA AGRICOLA - Academic
DatabaseTitleList	AGRICOLA
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography Engineering
EISSN	1872-8235
EndPage	200
ExternalDocumentID	10_1016_j_isprsjprs_2020_06_003 S0924271620301532
GeographicLocations	China
GeographicLocations_xml	– name: China
GroupedDBID	--K --M .~1 0R~ 1B1 1RT 1~. 1~5 29J 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABJNI ABMAC ABQEM ABQYD ABXDB ABYKQ ACDAQ ACGFS ACLVX ACNNM ACRLP ACSBN ACZNC ADBBV ADEZE ADJOM ADMUD AEBSH AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG ATOGT AVWKF AXJTR AZFZN BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q G8K GBLVA GBOLZ HMA HVGLF HZ~ H~9 IHE IMUCA J1W KOM LY3 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG RNS ROL RPZ SDF SDG SEP SES SEW SPC SPCBC SSE SSV SSZ T5K T9H WUQ ZMT ~02 ~G- 9DU AAHBH AATTM AAXKI AAYWO AAYXX ABDPE ABUFD ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7S9 L.6
ID	FETCH-LOGICAL-c414t-9fa39b4e302b609fcc65331982ce47e3f6abd58ccce274bbf66e34b0465903943
ISICitedReferencesCount	893
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000551268300015&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0924-2716
IngestDate	Thu Oct 02 16:30:03 EDT 2025 Tue Nov 18 21:12:36 EST 2025 Sat Nov 29 07:19:46 EST 2025 Fri Feb 23 02:47:51 EST 2024
IsPeerReviewed	true
IsScholarly	true
Keywords	Deep supervision network Change detection High resolution remote sensing image Image difference discrimination Image fusion
Language	English
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c414t-9fa39b4e302b609fcc65331982ce47e3f6abd58ccce274bbf66e34b0465903943
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ORCID	0000-0003-3006-4542 0000-0002-7242-4473
PQID	2986045397
PQPubID	24069
PageCount	18
ParticipantIDs	proquest_miscellaneous_2986045397 crossref_primary_10_1016_j_isprsjprs_2020_06_003 crossref_citationtrail_10_1016_j_isprsjprs_2020_06_003 elsevier_sciencedirect_doi_10_1016_j_isprsjprs_2020_06_003
PublicationCentury	2000
PublicationDate	August 2020 2020-08-00 20200801
PublicationDateYYYYMMDD	2020-08-01
PublicationDate_xml	– month: 08 year: 2020 text: August 2020
PublicationDecade	2020
PublicationTitle	ISPRS journal of photogrammetry and remote sensing
PublicationYear	2020
Publisher	Elsevier B.V
Publisher_xml	– name: Elsevier B.V
References	Singh (b0175) 1989; 10 Daudt, R.C., Saux, B. Le, Boulch, A., Gousseau, Y., 2018. High Resolution Semantic Change Detection. arXiv preprint arXiv:1810.08452. Jackson (b0070) 1983; 13 Padron-Hidalgo, Laparra, Longbotham, Camps-Valls (b0140) 2019; 57 Shelhamer, Long, Darrell (b0165) 2017 Todd (b0185) 1977; 5 Lei, Zhang, Lv, Li, Liu, Nandi (b0100) 2019; 16 Jin, Yang, Danielson, Homer, Fry, Xian (b0080) 2013; 132 Lei, Liu, Shi, Lei, Wang (b0105) 2019 Deng, Wang, Deng, Qi (b0035) 2008; 16 Guo, E., Fu, X., Zhu, J., Deng, M., Liu, Y., Zhu, Q., Li, H., 2018. Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection. arXiv preprint arXiv:1810.09111. Luppino, L.T., Bianchi, F.M., Moser, G., Anfinsen, S.N., 2019. Unsupervised image regression for heterogeneous change detection. IEEE Trans. Geosci. Remote Sens. https://doi.org/10.1109/TGRS.2019.2930348. Woo, Park, Lee, Kweon (b0195) 2018 Glorot, Bordes, Bengio (b0050) 2011 Bromley, Guyon, LeCun, Säckinger, Shah (b0010) 1994 Kuncheva, Faithfull (b0085) 2014; 25 Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. Jia Deng, Wei Dong, Socher, R., Li-Jia Li, Kai Li, Li Fei-Fei, 2009. ImageNet: A large-scale hierarchical image database. pp. 248-255. https://doi.org/10.1109/cvprw.2009.5206848. He, Zhang, Ren, Sun (b0060) 2015 Wang, Xu (b0190) 2010; 162 Zhou, Rahman Siddiquee, Tajbakhsh, Liang (b0215) 2018 Lebedev, Vizilter, Vygolov, Knyaz, Rubis (b0090) 2018; XLII–2 Mundia, Aniya (b0135) 2005; 26 Glorot, Bengio (b0045) 2010; 9 El Amin, A.M., Liu, Q., Wang, Y., 2017. Zoom out CNNs features for optical remote sensing change detection, in: 2017 2nd International Conference on Image, Vision and Computing, ICIVC 2017. 2, 812-817. https://doi.org/10.1109/ICIVC.2017.7984667. Mao, T., Liu, W., Zhao, Y., Huang, J., 2018. Change Detection in Semantic Level for SAR Images, in: 2018 3rd IEEE International Conference on Image, Vision and Computing, ICIVC 2018. https://doi.org/10.1109/ICIVC.2018.8492796. Hou, Wang, Liu (b0065) 2017; 14 Peng, Guan (b0145) 2019; 13 Celik (b0020) 2009; 6 Chen, Hay, Carvalho, Wulder (b0025) 2012; 33 Zhang, Gong, Zhang, Su, Shi (b0210) 2016; 13 Long, Shelhamer, Darrell (b0115) 2015 Ronneberger, O., Fischer, P., Brox, T., 2015. U-net: Convolutional networks for biomedical image segmentation, in: Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-24574-4_28. Zerrouki, Harrou, Sun (b0205) 2018; 15 Liu, Gong, Qin, Tan (b0110) 2020 Lv, Chen, Qiu, Sangaiah (b0125) 2018; 14 Wu, Du, Cui, Zhang (b0200) 2017; 199 Alcantarilla, Stent, Ros, Arroyo, Gherardi (b0005) 2018; 42 Lee, Xie, Gallagher, Zhang, Tu (b0095) 2015 Peng, Zhang, Guan (b0150) 2019; 11 Saha, S., Bovolo, F., Bruzzone, L., 2019. Unsupervised deep change vector analysis for multiple-change detection in VHR Images. IEEE Trans. Geosci. Remote Sens. 57, 3677-3693. https://doi.org/10.1109/TGRS.2018.2886643Saha, S., Bovolo, F., Bruzzone, L., 2019. Unsupervised Multiple-Change Detection in VHR Multisensor Images Via Deep-Learning Based Adaptation, in: IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 5033–5036. https://doi.org/10.1109/igarss.2019.8900173. Caye Daudt, Le Saux, Boulch (b0015) 2018 Singh (b0180) 1986; Manag 10.1016/j.isprsjprs.2020.06.003_b0075 10.1016/j.isprsjprs.2020.06.003_b0130 10.1016/j.isprsjprs.2020.06.003_b0030 Long (10.1016/j.isprsjprs.2020.06.003_b0115) 2015 Mundia (10.1016/j.isprsjprs.2020.06.003_b0135) 2005; 26 10.1016/j.isprsjprs.2020.06.003_b0170 Glorot (10.1016/j.isprsjprs.2020.06.003_b0050) 2011 Caye Daudt (10.1016/j.isprsjprs.2020.06.003_b0015) 2018 Padron-Hidalgo (10.1016/j.isprsjprs.2020.06.003_b0140) 2019; 57 Alcantarilla (10.1016/j.isprsjprs.2020.06.003_b0005) 2018; 42 Deng (10.1016/j.isprsjprs.2020.06.003_b0035) 2008; 16 Lei (10.1016/j.isprsjprs.2020.06.003_b0100) 2019; 16 Lv (10.1016/j.isprsjprs.2020.06.003_b0125) 2018; 14 Wu (10.1016/j.isprsjprs.2020.06.003_b0200) 2017; 199 Glorot (10.1016/j.isprsjprs.2020.06.003_b0045) 2010; 9 Celik (10.1016/j.isprsjprs.2020.06.003_b0020) 2009; 6 Shelhamer (10.1016/j.isprsjprs.2020.06.003_b0165) 2017 Lee (10.1016/j.isprsjprs.2020.06.003_b0095) 2015 Peng (10.1016/j.isprsjprs.2020.06.003_b0145) 2019; 13 Bromley (10.1016/j.isprsjprs.2020.06.003_b0010) 1994 Hou (10.1016/j.isprsjprs.2020.06.003_b0065) 2017; 14 Kuncheva (10.1016/j.isprsjprs.2020.06.003_b0085) 2014; 25 Woo (10.1016/j.isprsjprs.2020.06.003_b0195) 2018 Zhou (10.1016/j.isprsjprs.2020.06.003_b0215) 2018 Todd (10.1016/j.isprsjprs.2020.06.003_b0185) 1977; 5 Lebedev (10.1016/j.isprsjprs.2020.06.003_b0090) 2018; XLII–2 Jin (10.1016/j.isprsjprs.2020.06.003_b0080) 2013; 132 Liu (10.1016/j.isprsjprs.2020.06.003_b0110) 2020 Singh (10.1016/j.isprsjprs.2020.06.003_b0175) 1989; 10 10.1016/j.isprsjprs.2020.06.003_b0120 10.1016/j.isprsjprs.2020.06.003_b0040 Chen (10.1016/j.isprsjprs.2020.06.003_b0025) 2012; 33 10.1016/j.isprsjprs.2020.06.003_b0160 Lei (10.1016/j.isprsjprs.2020.06.003_b0105) 2019 Singh (10.1016/j.isprsjprs.2020.06.003_b0180) 1986; Manag Zerrouki (10.1016/j.isprsjprs.2020.06.003_b0205) 2018; 15 Jackson (10.1016/j.isprsjprs.2020.06.003_b0070) 1983; 13 Peng (10.1016/j.isprsjprs.2020.06.003_b0150) 2019; 11 Zhang (10.1016/j.isprsjprs.2020.06.003_b0210) 2016; 13 He (10.1016/j.isprsjprs.2020.06.003_b0060) 2015 Wang (10.1016/j.isprsjprs.2020.06.003_b0190) 2010; 162 10.1016/j.isprsjprs.2020.06.003_b0055 10.1016/j.isprsjprs.2020.06.003_b0155
References_xml	– start-page: 640 year: 2017 end-page: 651 ident: b0165 article-title: Fully Convolutional Networks for Semantic Segmentation publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence. – volume: 10 start-page: 989 year: 1989 end-page: 1003 ident: b0175 article-title: Review Articlel: Digital change detection techniques using remotely-sensed data publication-title: Int. J. Remote Sens. – reference: Guo, E., Fu, X., Zhu, J., Deng, M., Liu, Y., Zhu, Q., Li, H., 2018. Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection. arXiv preprint arXiv:1810.09111. – volume: 11 start-page: 1382 year: 2019 ident: b0150 article-title: End-to-end change detection for high resolution satellite images using improved UNet++ publication-title: Remote Sens. – start-page: 4063 year: 2018 end-page: 4067 ident: b0015 article-title: Fully convolutional siamese networks for change detection publication-title: in: Proceedings - International Conference on Image Processing, ICIP – volume: 25 start-page: 69 year: 2014 end-page: 80 ident: b0085 article-title: PCA feature extraction for change detection in multidimensional unlabeled data publication-title: IEEE Trans. Neural Networks Learn. Syst. – volume: 5 start-page: 529 year: 1977 end-page: 534 ident: b0185 article-title: Urban and regional land use change detected by using Landsat data publication-title: J. Res. US Geol. Surv. – volume: 15 start-page: 927 year: 2018 end-page: 931 ident: b0205 article-title: Statistical Monitoring of Changes to Land Cover publication-title: IEEE Geosci. Remote Sens. Lett. – start-page: 737 year: 1994 end-page: 744 ident: b0010 article-title: Signature verification using a“ siamese” time delay neural network publication-title: InAdvances in neural information processing systems. – start-page: 315 year: 2011 end-page: 323 ident: b0050 article-title: Deep sparse rectifier neural networks publication-title: Journal of Machine Learning Research. – start-page: 36600 year: 2019 end-page: 36616 ident: b0105 article-title: Multiscale Superpixel Segmentation with Deep Features for Change Detection. IEEE publication-title: Access. – reference: Saha, S., Bovolo, F., Bruzzone, L., 2019. Unsupervised deep change vector analysis for multiple-change detection in VHR Images. IEEE Trans. Geosci. Remote Sens. 57, 3677-3693. https://doi.org/10.1109/TGRS.2018.2886643Saha, S., Bovolo, F., Bruzzone, L., 2019. Unsupervised Multiple-Change Detection in VHR Multisensor Images Via Deep-Learning Based Adaptation, in: IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 5033–5036. https://doi.org/10.1109/igarss.2019.8900173. – volume: 199 start-page: 241 year: 2017 end-page: 255 ident: b0200 article-title: A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion publication-title: Remote Sens. Environ. – start-page: 3 year: 2018 end-page: 11 ident: b0215 article-title: Unet++: A nested u-net architecture for medical image segmentation publication-title: Lecture Notes in Computer Science. – volume: 14 start-page: 2418 year: 2017 end-page: 2422 ident: b0065 article-title: Change Detection Based on Deep Features and Low Rank publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 16 start-page: 4823 year: 2008 end-page: 4838 ident: b0035 article-title: PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data publication-title: Int. J. Remote Sens. – volume: Manag start-page: 237 year: 1986 end-page: 254 ident: b0180 article-title: Change detection in the tropical forest environment of northeastern India using Landsat publication-title: Remote Sens. Trop. L. – reference: Mao, T., Liu, W., Zhao, Y., Huang, J., 2018. Change Detection in Semantic Level for SAR Images, in: 2018 3rd IEEE International Conference on Image, Vision and Computing, ICIVC 2018. https://doi.org/10.1109/ICIVC.2018.8492796. – volume: 26 start-page: 2831 year: 2005 end-page: 2849 ident: b0135 article-title: Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS publication-title: Int. J. Remote Sens. – reference: Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. – start-page: 1026 year: 2015 end-page: 1034 ident: b0060 article-title: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification publication-title: The IEEE International Conference on Computer Vision. – start-page: 3431 year: 2015 end-page: 3440 ident: b0115 article-title: Fully Convolutional Networks for Semantic Segmentation publication-title: The IEEE Conference on Computer Vision and Pattern Recognition. – reference: Daudt, R.C., Saux, B. Le, Boulch, A., Gousseau, Y., 2018. High Resolution Semantic Change Detection. arXiv preprint arXiv:1810.08452. – volume: 162 start-page: 311 year: 2010 end-page: 326 ident: b0190 article-title: Comparison of remote sensing change detection techniques for assessing hurricane damage to forests publication-title: Environ. Monit. Assess. – reference: El Amin, A.M., Liu, Q., Wang, Y., 2017. Zoom out CNNs features for optical remote sensing change detection, in: 2017 2nd International Conference on Image, Vision and Computing, ICIVC 2017. 2, 812-817. https://doi.org/10.1109/ICIVC.2017.7984667. – start-page: 562 year: 2015 end-page: 570 ident: b0095 article-title: Deeply-supervised nets publication-title: Artificial intelligence and statistics. – reference: Luppino, L.T., Bianchi, F.M., Moser, G., Anfinsen, S.N., 2019. Unsupervised image regression for heterogeneous change detection. IEEE Trans. Geosci. Remote Sens. https://doi.org/10.1109/TGRS.2019.2930348. – volume: 33 start-page: 4434 year: 2012 end-page: 4457 ident: b0025 article-title: Object-based change detection publication-title: Int. J. Remote Sens. – reference: Ronneberger, O., Fischer, P., Brox, T., 2015. U-net: Convolutional networks for biomedical image segmentation, in: Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-24574-4_28. – volume: 13 start-page: 1666 year: 2016 end-page: 1670 ident: b0210 article-title: Feature-Level Change Detection Using Deep Representation and Feature Change Analysis for Multispectral Imagery publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 42 start-page: 1301 year: 2018 end-page: 1322 ident: b0005 article-title: Street-view change detection with deconvolutional networks publication-title: Auton. Robots. – volume: 14 start-page: 5530 year: 2018 end-page: 5538 ident: b0125 article-title: Deep Learning and Superpixel Feature Extraction Based on Contractive Autoencoder for Change Detection in SAR Images publication-title: IEEE Trans. Ind. Informatics. – volume: 9 start-page: 249 year: 2010 end-page: 256 ident: b0045 article-title: Understanding the difficulty of training deep feedforward neural networks publication-title: Proceedings of the thirteenth international conference on artificial intelligence and statistics. – volume: 57 start-page: 7743 year: 2019 end-page: 7755 ident: b0140 article-title: Kernel Anomalous Change Detection for Remote Sensing Imagery publication-title: IEEE Trans. Geosci. Remote Sens. – start-page: 3 year: 2018 end-page: 19 ident: b0195 article-title: CBAM: Convolutional block attention module publication-title: Lecture Notes in Computer Science. – volume: 13 start-page: 409 year: 1983 end-page: 421 ident: b0070 article-title: Spectral indices in N-Space publication-title: Remote Sens. Environ. – volume: 16 start-page: 982 year: 2019 end-page: 986 ident: b0100 article-title: Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 132 start-page: 159 year: 2013 end-page: 175 ident: b0080 article-title: A comprehensive change detection method for updating the National Land Cover Database to circa 2011 publication-title: Remote Sens. Environ. – volume: 13 year: 2019 ident: b0145 article-title: Unsupervised change detection method based on saliency analysis and convolutional neural network publication-title: J. Appl. Remote Sens. – reference: Jia Deng, Wei Dong, Socher, R., Li-Jia Li, Kai Li, Li Fei-Fei, 2009. ImageNet: A large-scale hierarchical image database. pp. 248-255. https://doi.org/10.1109/cvprw.2009.5206848. – volume: 6 start-page: 772 year: 2009 end-page: 776 ident: b0020 article-title: Unsupervised change detection in satellite images using principal component analysis and κ-means clustering publication-title: IEEE Geosci. Remote Sens. Lett. – year: 2020 ident: b0110 article-title: Bipartite Differential Neural Network for Unsupervised Image Change Detection – volume: XLII–2 start-page: 565 year: 2018 end-page: 571 ident: b0090 article-title: Change detection in remote sensing images using conditional adversarial networks. ISPRS - Int. Arch. Photogramm. Remote Sens. Spat publication-title: Inf. Sci. – ident: 10.1016/j.isprsjprs.2020.06.003_b0120 doi: 10.1109/TGRS.2019.2930348 – ident: 10.1016/j.isprsjprs.2020.06.003_b0055 – ident: 10.1016/j.isprsjprs.2020.06.003_b0170 – volume: 15 start-page: 927 year: 2018 ident: 10.1016/j.isprsjprs.2020.06.003_b0205 article-title: Statistical Monitoring of Changes to Land Cover publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2018.2817522 – volume: 132 start-page: 159 year: 2013 ident: 10.1016/j.isprsjprs.2020.06.003_b0080 article-title: A comprehensive change detection method for updating the National Land Cover Database to circa 2011 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2013.01.012 – start-page: 3 year: 2018 ident: 10.1016/j.isprsjprs.2020.06.003_b0195 article-title: CBAM: Convolutional block attention module publication-title: Lecture Notes in Computer Science. doi: 10.1007/978-3-030-01234-2_1 – ident: 10.1016/j.isprsjprs.2020.06.003_b0030 – volume: 13 start-page: 409 year: 1983 ident: 10.1016/j.isprsjprs.2020.06.003_b0070 article-title: Spectral indices in N-Space publication-title: Remote Sens. Environ. doi: 10.1016/0034-4257(83)90010-X – ident: 10.1016/j.isprsjprs.2020.06.003_b0075 doi: 10.1109/CVPRW.2009.5206848 – start-page: 4063 year: 2018 ident: 10.1016/j.isprsjprs.2020.06.003_b0015 article-title: Fully convolutional siamese networks for change detection – volume: 6 start-page: 772 year: 2009 ident: 10.1016/j.isprsjprs.2020.06.003_b0020 article-title: Unsupervised change detection in satellite images using principal component analysis and κ-means clustering publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2009.2025059 – volume: 9 start-page: 249 year: 2010 ident: 10.1016/j.isprsjprs.2020.06.003_b0045 article-title: Understanding the difficulty of training deep feedforward neural networks publication-title: Proceedings of the thirteenth international conference on artificial intelligence and statistics. – start-page: 3431 year: 2015 ident: 10.1016/j.isprsjprs.2020.06.003_b0115 article-title: Fully Convolutional Networks for Semantic Segmentation publication-title: The IEEE Conference on Computer Vision and Pattern Recognition. – start-page: 1026 year: 2015 ident: 10.1016/j.isprsjprs.2020.06.003_b0060 article-title: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification publication-title: The IEEE International Conference on Computer Vision. – volume: 13 start-page: 1666 year: 2016 ident: 10.1016/j.isprsjprs.2020.06.003_b0210 article-title: Feature-Level Change Detection Using Deep Representation and Feature Change Analysis for Multispectral Imagery publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2016.2601930 – volume: 33 start-page: 4434 year: 2012 ident: 10.1016/j.isprsjprs.2020.06.003_b0025 article-title: Object-based change detection publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2011.648285 – start-page: 36600 year: 2019 ident: 10.1016/j.isprsjprs.2020.06.003_b0105 article-title: Multiscale Superpixel Segmentation with Deep Features for Change Detection. IEEE publication-title: Access. doi: 10.1109/ACCESS.2019.2902613 – volume: XLII–2 start-page: 565 year: 2018 ident: 10.1016/j.isprsjprs.2020.06.003_b0090 article-title: Change detection in remote sensing images using conditional adversarial networks. ISPRS - Int. Arch. Photogramm. Remote Sens. Spat publication-title: Inf. Sci. – ident: 10.1016/j.isprsjprs.2020.06.003_b0155 doi: 10.1007/978-3-319-24574-4_28 – start-page: 737 year: 1994 ident: 10.1016/j.isprsjprs.2020.06.003_b0010 article-title: Signature verification using a“ siamese” time delay neural network publication-title: InAdvances in neural information processing systems. – volume: 11 start-page: 1382 year: 2019 ident: 10.1016/j.isprsjprs.2020.06.003_b0150 article-title: End-to-end change detection for high resolution satellite images using improved UNet++ publication-title: Remote Sens. doi: 10.3390/rs11111382 – start-page: 3 year: 2018 ident: 10.1016/j.isprsjprs.2020.06.003_b0215 article-title: Unet++: A nested u-net architecture for medical image segmentation publication-title: Lecture Notes in Computer Science. – volume: 57 start-page: 7743 year: 2019 ident: 10.1016/j.isprsjprs.2020.06.003_b0140 article-title: Kernel Anomalous Change Detection for Remote Sensing Imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2916212 – volume: 10 start-page: 989 year: 1989 ident: 10.1016/j.isprsjprs.2020.06.003_b0175 article-title: Review Articlel: Digital change detection techniques using remotely-sensed data publication-title: Int. J. Remote Sens. doi: 10.1080/01431168908903939 – start-page: 315 year: 2011 ident: 10.1016/j.isprsjprs.2020.06.003_b0050 article-title: Deep sparse rectifier neural networks publication-title: Journal of Machine Learning Research. – volume: 14 start-page: 2418 year: 2017 ident: 10.1016/j.isprsjprs.2020.06.003_b0065 article-title: Change Detection Based on Deep Features and Low Rank publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2017.2766840 – volume: 26 start-page: 2831 year: 2005 ident: 10.1016/j.isprsjprs.2020.06.003_b0135 article-title: Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS publication-title: Int. J. Remote Sens. doi: 10.1080/01431160500117865 – year: 2020 ident: 10.1016/j.isprsjprs.2020.06.003_b0110 – volume: Manag start-page: 237 year: 1986 ident: 10.1016/j.isprsjprs.2020.06.003_b0180 article-title: Change detection in the tropical forest environment of northeastern India using Landsat publication-title: Remote Sens. Trop. L. – volume: 162 start-page: 311 year: 2010 ident: 10.1016/j.isprsjprs.2020.06.003_b0190 article-title: Comparison of remote sensing change detection techniques for assessing hurricane damage to forests publication-title: Environ. Monit. Assess. doi: 10.1007/s10661-009-0798-8 – ident: 10.1016/j.isprsjprs.2020.06.003_b0040 doi: 10.1109/ICIVC.2017.7984667 – volume: 25 start-page: 69 year: 2014 ident: 10.1016/j.isprsjprs.2020.06.003_b0085 article-title: PCA feature extraction for change detection in multidimensional unlabeled data publication-title: IEEE Trans. Neural Networks Learn. Syst. doi: 10.1109/TNNLS.2013.2248094 – start-page: 562 year: 2015 ident: 10.1016/j.isprsjprs.2020.06.003_b0095 article-title: Deeply-supervised nets publication-title: Artificial intelligence and statistics. – ident: 10.1016/j.isprsjprs.2020.06.003_b0160 doi: 10.1109/IGARSS.2019.8900173 – volume: 16 start-page: 982 year: 2019 ident: 10.1016/j.isprsjprs.2020.06.003_b0100 article-title: Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2018.2889307 – volume: 5 start-page: 529 year: 1977 ident: 10.1016/j.isprsjprs.2020.06.003_b0185 article-title: Urban and regional land use change detected by using Landsat data publication-title: J. Res. US Geol. Surv. – volume: 199 start-page: 241 year: 2017 ident: 10.1016/j.isprsjprs.2020.06.003_b0200 article-title: A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.07.009 – start-page: 640 year: 2017 ident: 10.1016/j.isprsjprs.2020.06.003_b0165 article-title: Fully Convolutional Networks for Semantic Segmentation publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence. doi: 10.1109/TPAMI.2016.2572683 – ident: 10.1016/j.isprsjprs.2020.06.003_b0130 doi: 10.1109/ICIVC.2018.8492796 – volume: 13 year: 2019 ident: 10.1016/j.isprsjprs.2020.06.003_b0145 article-title: Unsupervised change detection method based on saliency analysis and convolutional neural network publication-title: J. Appl. Remote Sens. doi: 10.1117/1.JRS.13.024512 – volume: 42 start-page: 1301 year: 2018 ident: 10.1016/j.isprsjprs.2020.06.003_b0005 article-title: Street-view change detection with deconvolutional networks publication-title: Auton. Robots. doi: 10.1007/s10514-018-9734-5 – volume: 16 start-page: 4823 year: 2008 ident: 10.1016/j.isprsjprs.2020.06.003_b0035 article-title: PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data publication-title: Int. J. Remote Sens. doi: 10.1080/01431160801950162 – volume: 14 start-page: 5530 year: 2018 ident: 10.1016/j.isprsjprs.2020.06.003_b0125 article-title: Deep Learning and Superpixel Feature Extraction Based on Contractive Autoencoder for Change Detection in SAR Images publication-title: IEEE Trans. Ind. Informatics. doi: 10.1109/TII.2018.2873492
SSID	ssj0001568
Score	2.7208545
Snippet	Change detection in high resolution remote sensing images is crucial to the understanding of land surface changes. As traditional change detection methods are...
SourceID	proquest crossref elsevier
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	183
SubjectTerms	Change detection China cities data collection Deep supervision network High resolution remote sensing image Image difference discrimination Image fusion Internet remote sensing texture
Title	A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images
URI	https://dx.doi.org/10.1016/j.isprsjprs.2020.06.003 https://www.proquest.com/docview/2986045397
Volume	166
WOSCitedRecordID	wos000551268300015&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: ScienceDirect database customDbUrl: eissn: 1872-8235 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001568 issn: 0924-2716 databaseCode: AIEXJ dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3da9swEBehHWx7GFu3se4LDfZmPGxJlu29hdJtLaOUJYO8GVuWqUPqmNgu2d_Xf6wnS_7ostGNsYcYY6STyP1yvrvc_YTQ-5AHjAZJYnM_Tm3GE2bHhLi256ScEjfNXC9uD5vwz86CxSI8n0yuu16Yq5VfFMF2G5b_VdXwDJStWmf_Qt29UHgA96B0uILa4fpHip9aqZTl6odVNaUyBBW4lPmlKs3JGpUaswpd-d0WGOq-X5hRS9GVPSoGYwuicLNLK8ltQ2ClzgAA1UqrUmXvqhlGya3GDu7J7PzbbMxHUV6s67YE7FLWG032dFvITur66EIW2zxe9wap0Y1ochg7j5Wzr-3lOo3rfuxpboR8VTeiKcZZDTLU1JlU2067jc5ZEmYT3zXc2dpiBz6YdKI5T3qTzsdG2dVH5Zj3u2ZG3X116CzGElYqN9USPh_UvlpyV4cOb8u-hnGmdqM2Q1RU6VHwA_aJ74VgWvenJ8eL094hcHVHZr_7W2WGv1zud07ST-5C6wPNH6NHJnjBUw26J2giiwP0cERpeYDuf5aGBP0p2kyxhiIeoIhbyGANRWygiAGKWEMR91DEeYEVFPEARTyCItYowgZFWm71DH3_dDw_-mKbMz5swVxW22EW0zBhkjok4U6YCcEhAHHDgAjJfEkzHiepFwghJPFZkmScS8oSh3EvdGjI6HO0V6wL-QLhFFxtAeGFJwKHMRqD6AziG55lJBO-Iw4R777USBgCfHUOyyrqKh2XUa-NSGkjams-6SFy-oml5oC5e8rHTmuRcWW1ixoB3O6e_K7TcwTGXv2DFxdy3cCgMOAQg0EM8fJfFniFHgy_uddor9408g26J67qvNq8NfC9Af7C2wE
linkProvider	Elsevier
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+deeply+supervised+image+fusion+network+for+change+detection+in+high+resolution+bi-temporal+remote+sensing+images&rft.jtitle=ISPRS+journal+of+photogrammetry+and+remote+sensing&rft.au=Zhang%2C+Chenxiao&rft.au=Yue%2C+Peng&rft.au=Tapete%2C+Deodato&rft.au=Jiang%2C+Liangcun&rft.date=2020-08-01&rft.pub=Elsevier+B.V&rft.issn=0924-2716&rft.eissn=1872-8235&rft.volume=166&rft.spage=183&rft.epage=200&rft_id=info:doi/10.1016%2Fj.isprsjprs.2020.06.003&rft.externalDocID=S0924271620301532
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0924-2716&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0924-2716&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0924-2716&client=summon