PolSAR image classification based on multi-scale stacked sparse autoencoder
Recently, many deep learning methods are applied with the spatial information to learn features for polarimetric synthetic aperture radar (PolSAR) image classification. However, without considering the multi-scale information, the classification performance of these methods are limited. Hence, this...
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| Vydáno v: | Neurocomputing (Amsterdam) Ročník 351; s. 167 - 179 |
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| Jazyk: | angličtina |
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Elsevier B.V
25.07.2019
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | Recently, many deep learning methods are applied with the spatial information to learn features for polarimetric synthetic aperture radar (PolSAR) image classification. However, without considering the multi-scale information, the classification performance of these methods are limited. Hence, this paper proposes a multi-scale feature extraction method based on stacked sparse autoencoder (SSAE), named the multi-scale SSAE (MS-SSAE), to improve the classification performance. This method extracts the deep multi-scale features by a two-stage framework. In the first stage, the SSAE uses training data at different scales to extract the multi-scale features. Then, a 1-D average pooling strategy is proposed to reduce the feature dimensionality at the second stage. Therefore, the MS-SSAE can capture discriminative multi-scale features. The experimental results certify that the MS-SSAE can not only improve the classification accuracy, but also remain the details in the image. |
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| AbstractList | Recently, many deep learning methods are applied with the spatial information to learn features for polarimetric synthetic aperture radar (PolSAR) image classification. However, without considering the multi-scale information, the classification performance of these methods are limited. Hence, this paper proposes a multi-scale feature extraction method based on stacked sparse autoencoder (SSAE), named the multi-scale SSAE (MS-SSAE), to improve the classification performance. This method extracts the deep multi-scale features by a two-stage framework. In the first stage, the SSAE uses training data at different scales to extract the multi-scale features. Then, a 1-D average pooling strategy is proposed to reduce the feature dimensionality at the second stage. Therefore, the MS-SSAE can capture discriminative multi-scale features. The experimental results certify that the MS-SSAE can not only improve the classification accuracy, but also remain the details in the image. |
| Author | Ma, Wenping Zhang, Lu Duan, Yiping Jiao, Licheng Zhang, Dan |
| Author_xml | – sequence: 1 givenname: Lu orcidid: 0000-0003-4764-368X surname: Zhang fullname: Zhang, Lu email: oleand_er@126.com organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province 710071, China – sequence: 2 givenname: Licheng surname: Jiao fullname: Jiao, Licheng organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province 710071, China – sequence: 3 givenname: Wenping surname: Ma fullname: Ma, Wenping organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province 710071, China – sequence: 4 givenname: Yiping surname: Duan fullname: Duan, Yiping organization: Department of Electronic Engineering, Tsinghua University, Beijing, China – sequence: 5 givenname: Dan surname: Zhang fullname: Zhang, Dan organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province 710071, China |
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| Cites_doi | 10.1080/01431169408954244 10.1109/LGRS.2018.2829182 10.1109/JSTARS.2016.2553104 10.1109/TGRS.2013.2291940 10.1109/TGRS.2011.2160647 10.1109/JSTARS.2014.2329330 10.1109/TMI.2015.2458702 10.1109/JSTARS.2017.2698076 10.1126/science.1127647 10.1109/LGRS.2016.2618840 10.1109/TGRS.2016.2514504 10.1561/2200000006 10.1109/36.673687 10.1109/TIFS.2015.2446438 10.1109/LGRS.2016.2586109 10.1109/TGRS.2017.2743222 10.1109/36.214928 10.1109/TGRS.2003.813494 10.1109/TGRS.2013.2258675 10.1109/TGRS.2006.886176 10.1109/36.485127 10.1109/JSTARS.2016.2532922 10.1109/MCI.2010.938364 10.1109/LGRS.2008.2002263 |
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| Keywords | Deep learning Feature learning Multi-scale Polsar classification Stacked sparse autoencoder |
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| References | LeCun, Kavukcuoglu, Farabet (bib0015) 2010 Wu, Ji, Yu, Su (bib0020) 2008; 5 Hou, Kou, Jiao (bib0022) 2016; 9 Huang, Huang (bib0030) 2014 Jun, Xiang (bib0018) 2016; 35 Liu, Zhu, Yang, Hou, Gou, Xiong, Jiao (bib0007) 2016; 9 Shang, Hirose (bib0003) 2014; 52 Liu, Yang, Gou, Zhu, Wang, Jiao (bib0005) 2016; 10 Arel, Rose, Karnowski (bib0014) 2010; 5 Bengio, Lamblin, Popovici, Larochelle (bib0012) 2007; 19 Chen, Lin, Zhao, Wang, Gu (bib0016) 2014; 7 Xie, Wang, Liu, Lin, Hou (bib0019) 2014 Alonso-Gonzalez, Lopez-Martinez, Salembier (bib0027) 2012; 50 Lee, Grunes, Kwok (bib0004) 1994; 15 Freeman, Durden (bib0008) 1998; 36 Fukuda, Hirosawa (bib0001) 2001; 1 Gao, Zhang (bib0017) 2015; 10 Azimi-Sadjadi, Ghaloum, Zoughi (bib0002) 1991; 31 Hinton, Salakhutdinov (bib0011) 2006; 313 Touzi (bib0009) 2007; 45 Sun, Zhang (bib0029) 2011 Liu, Jiao, Hou, Yang (bib0025) 2016; 54 Zhou, Cui (bib0028) 2009; 1 Uhlmann, Kiranyaz (bib0006) 2014; 52 Zhang, Wang (bib0032) 2017; 55 Xie, Jiao, Hou (bib0024) 2017; 10 Zhou, Wang (bib0026) 2017; 13 Cloude, Pottier (bib0010) 1996; 34 Chen, Chen, Lee (bib0031) 2003; 41 Bengio (bib0013) 2009; 2 Hu, Fan (bib0023) 2018; 15 Zhang, Ma, Zhang (bib0021) 2016; 13 Freeman (10.1016/j.neucom.2019.03.024_bib0008) 1998; 36 Arel (10.1016/j.neucom.2019.03.024_bib0014) 2010; 5 Zhou (10.1016/j.neucom.2019.03.024_bib0026) 2017; 13 Alonso-Gonzalez (10.1016/j.neucom.2019.03.024_bib0027) 2012; 50 Wu (10.1016/j.neucom.2019.03.024_bib0020) 2008; 5 Xie (10.1016/j.neucom.2019.03.024_bib0019) 2014 Hinton (10.1016/j.neucom.2019.03.024_bib0011) 2006; 313 LeCun (10.1016/j.neucom.2019.03.024_bib0015) 2010 Bengio (10.1016/j.neucom.2019.03.024_bib0013) 2009; 2 Uhlmann (10.1016/j.neucom.2019.03.024_bib0006) 2014; 52 Liu (10.1016/j.neucom.2019.03.024_bib0005) 2016; 10 Cloude (10.1016/j.neucom.2019.03.024_bib0010) 1996; 34 Jun (10.1016/j.neucom.2019.03.024_bib0018) 2016; 35 Huang (10.1016/j.neucom.2019.03.024_bib0030) 2014 Fukuda (10.1016/j.neucom.2019.03.024_bib0001) 2001; 1 Bengio (10.1016/j.neucom.2019.03.024_bib0012) 2007; 19 Azimi-Sadjadi (10.1016/j.neucom.2019.03.024_bib0002) 1991; 31 Gao (10.1016/j.neucom.2019.03.024_bib0017) 2015; 10 Hou (10.1016/j.neucom.2019.03.024_bib0022) 2016; 9 Chen (10.1016/j.neucom.2019.03.024_bib0016) 2014; 7 Hu (10.1016/j.neucom.2019.03.024_bib0023) 2018; 15 Liu (10.1016/j.neucom.2019.03.024_bib0007) 2016; 9 Sun (10.1016/j.neucom.2019.03.024_bib0029) 2011 Lee (10.1016/j.neucom.2019.03.024_bib0004) 1994; 15 Zhang (10.1016/j.neucom.2019.03.024_bib0021) 2016; 13 Shang (10.1016/j.neucom.2019.03.024_bib0003) 2014; 52 Zhang (10.1016/j.neucom.2019.03.024_bib0032) 2017; 55 Xie (10.1016/j.neucom.2019.03.024_bib0024) 2017; 10 Liu (10.1016/j.neucom.2019.03.024_bib0025) 2016; 54 Touzi (10.1016/j.neucom.2019.03.024_bib0009) 2007; 45 Chen (10.1016/j.neucom.2019.03.024_bib0031) 2003; 41 Zhou (10.1016/j.neucom.2019.03.024_bib0028) 2009; 1 |
| References_xml | – volume: 10 start-page: 3604 year: 2017 end-page: 3615 ident: bib0024 article-title: POLSAR image classification via Wishart-AE model or Wishart-CAE model publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. – volume: 1 start-page: 187 year: 2001 end-page: 189 ident: bib0001 article-title: Support vector machine classification of land cover: application to polarimetric SAR data publication-title: Proceedings of the IGARSS. IEEE – volume: 50 start-page: 593 year: 2012 end-page: 605 ident: bib0027 article-title: Filtering and segmentation of polarimetric SAR data based on binary partition trees publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 5 start-page: 13 year: 2010 end-page: 18 ident: bib0014 article-title: Deep machine learning-a new frontier in artificial intelligence research [research frontier] publication-title: IEEE Comput. Intell. Mag. – volume: 13 start-page: 1935 year: 2017 end-page: 1939 ident: bib0026 article-title: Polarimetric SAR image classification using deep convolutional neural networks publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 55 start-page: 7177 year: 2017 end-page: 7188 ident: bib0032 article-title: Complex-valued convolutional neural network and its application in polarimetric SAR image classification publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 15 start-page: 2299 year: 1994 end-page: 2311 ident: bib0004 article-title: Classification of multilook polarimetric SAR imagery based on complex wishart distribution publication-title: Int. J. Remote Sens. – volume: 9 start-page: 3072 year: 2016 end-page: 3081 ident: bib0022 article-title: Classification of polarimetric SAR images using multilayer autoencoders and superpixels publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. – volume: 13 start-page: 1359 year: 2016 end-page: 1363 ident: bib0021 article-title: Stacked sparse autoencoder in PolSAR data classification using local spatial information publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 19 start-page: 153 year: 2007 end-page: 160 ident: bib0012 article-title: Greedy layer-wise training of deep networks publication-title: Adv. Neural Inf. Process. Syst. – volume: 45 start-page: 73 year: 2007 end-page: 84 ident: bib0009 article-title: Target scattering decomposition in terms of roll-invariant target param- eters publication-title: IEEE Trans. Geosci. Remote Sens. – start-page: 1 year: 2011 end-page: 4 ident: bib0029 article-title: An IHS fusion method integrated by the nonsubsampled contourlet transform to fuse the airborne X-band InSAR and P-band full PolSAR images publication-title: 2011 International Symposium on Image and Data Fusion (ISIDF) – volume: 52 start-page: 2197 year: 2014 end-page: 2216 ident: bib0006 article-title: Integrating color features in polarimetric SAR image classification publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 34 start-page: 498 year: 1996 end-page: 518 ident: bib0010 article-title: A review of target decomposition theorems in radar polarimetry publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 7 start-page: 2094 year: 2014 end-page: 2107 ident: bib0016 article-title: Deep learning-based classification of hyperspectral data publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. – volume: 35 start-page: 119 year: 2016 end-page: 130 ident: bib0018 article-title: Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images publication-title: IEEE Trans. Med. Imaging – volume: 1 start-page: 1 year: 2009 end-page: 4 ident: bib0028 article-title: A new edge detection method of polarimetric SAR images using the curvelet transform and the Duda operator publication-title: Proceedings of the IET International Radar Conference – start-page: 253 year: 2010 end-page: 256 ident: bib0015 article-title: Convolutional networks and applications in vision publication-title: Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS) – volume: 31 start-page: 511 year: 1991 end-page: 515 ident: bib0002 article-title: Terrain classification in SAR images using principal components analysis and neural networks publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 9 start-page: 3001 year: 2016 end-page: 3015 ident: bib0007 article-title: Semisupervised feature extraction with neighborhood constraints for polarimetric SAR classification publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. – volume: 15 start-page: 1050 year: 2018 end-page: 1054 ident: bib0023 article-title: Classification of PolSAR images based on adaptive nonlocal stacked sparse autoencoder publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 10 start-page: 1 year: 2016 end-page: 11 ident: bib0005 article-title: Polarimetric SAR feature extraction with neighborhood preservation-based deep learning publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. – volume: 5 start-page: 668 year: 2008 end-page: 672 ident: bib0020 article-title: Region-based classification of polarimetric SAR images using Wishart MRF publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 2 start-page: 1 year: 2009 end-page: 127 ident: bib0013 article-title: Learning deep architectures for AI publication-title: Found. Trends – start-page: 4572 year: 2014 end-page: 4575 ident: bib0030 article-title: Saliency detection based on distance between patches in polarimetric SAR images publication-title: IEEE International Conference on Geoscience and Remote Sensing Symposium – volume: 54 start-page: 3292 year: 2016 end-page: 3308 ident: bib0025 article-title: Pol-SAR image classification based on Wishart DBN and local spatial information publication-title: IEEE Trans. Geosci. Remote Sens. – start-page: 2818 year: 2014 end-page: 2821 ident: bib0019 article-title: Multilayer feature learning for polarimetric synthetic radar data classification publication-title: IEEE International Conference on Geoscience and Remote Sensing Symposium – volume: 41 start-page: 2089 year: 2003 end-page: 2100 ident: bib0031 article-title: The use of fully polarimetric information for the fuzzy neural classification of SAR images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 36 start-page: 963 year: 1998 end-page: 973 ident: bib0008 article-title: A three-component scattering model for polari- metric SAR data publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 313 start-page: 504 year: 2006 end-page: 507 ident: bib0011 article-title: Reducing the dimensionalityality of data with neural networks publication-title: Science – volume: 52 start-page: 5693 year: 2014 end-page: 5703 ident: bib0003 article-title: Quaternion neural-network-based PolSAR land classification in Poincare-sphere-parameter space publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 10 start-page: 2108 year: 2015 end-page: 2118 ident: bib0017 article-title: Single sample face recognition via learning deep supervised autoencoders publication-title: IEEE Trans. Inf. Forens. Secur. – volume: 15 start-page: 2299 year: 1994 ident: 10.1016/j.neucom.2019.03.024_bib0004 article-title: Classification of multilook polarimetric SAR imagery based on complex wishart distribution publication-title: Int. J. Remote Sens. doi: 10.1080/01431169408954244 – volume: 15 start-page: 1050 year: 2018 ident: 10.1016/j.neucom.2019.03.024_bib0023 article-title: Classification of PolSAR images based on adaptive nonlocal stacked sparse autoencoder publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2018.2829182 – volume: 9 start-page: 3072 year: 2016 ident: 10.1016/j.neucom.2019.03.024_bib0022 article-title: Classification of polarimetric SAR images using multilayer autoencoders and superpixels publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. doi: 10.1109/JSTARS.2016.2553104 – volume: 52 start-page: 5693 year: 2014 ident: 10.1016/j.neucom.2019.03.024_bib0003 article-title: Quaternion neural-network-based PolSAR land classification in Poincare-sphere-parameter space publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2013.2291940 – volume: 50 start-page: 593 year: 2012 ident: 10.1016/j.neucom.2019.03.024_bib0027 article-title: Filtering and segmentation of polarimetric SAR data based on binary partition trees publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2011.2160647 – volume: 7 start-page: 2094 year: 2014 ident: 10.1016/j.neucom.2019.03.024_bib0016 article-title: Deep learning-based classification of hyperspectral data publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. doi: 10.1109/JSTARS.2014.2329330 – volume: 35 start-page: 119 year: 2016 ident: 10.1016/j.neucom.2019.03.024_bib0018 article-title: Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2015.2458702 – start-page: 1 year: 2011 ident: 10.1016/j.neucom.2019.03.024_bib0029 article-title: An IHS fusion method integrated by the nonsubsampled contourlet transform to fuse the airborne X-band InSAR and P-band full PolSAR images – start-page: 253 year: 2010 ident: 10.1016/j.neucom.2019.03.024_bib0015 article-title: Convolutional networks and applications in vision – volume: 10 start-page: 3604 year: 2017 ident: 10.1016/j.neucom.2019.03.024_bib0024 article-title: POLSAR image classification via Wishart-AE model or Wishart-CAE model publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. doi: 10.1109/JSTARS.2017.2698076 – start-page: 4572 year: 2014 ident: 10.1016/j.neucom.2019.03.024_bib0030 article-title: Saliency detection based on distance between patches in polarimetric SAR images – volume: 313 start-page: 504 year: 2006 ident: 10.1016/j.neucom.2019.03.024_bib0011 article-title: Reducing the dimensionalityality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – volume: 13 start-page: 1935 year: 2017 ident: 10.1016/j.neucom.2019.03.024_bib0026 article-title: Polarimetric SAR image classification using deep convolutional neural networks publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2016.2618840 – volume: 1 start-page: 187 year: 2001 ident: 10.1016/j.neucom.2019.03.024_bib0001 article-title: Support vector machine classification of land cover: application to polarimetric SAR data – volume: 54 start-page: 3292 year: 2016 ident: 10.1016/j.neucom.2019.03.024_bib0025 article-title: Pol-SAR image classification based on Wishart DBN and local spatial information publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2016.2514504 – volume: 2 start-page: 1 issue: 1 year: 2009 ident: 10.1016/j.neucom.2019.03.024_bib0013 article-title: Learning deep architectures for AI publication-title: Found. Trends® Mach. Learn. doi: 10.1561/2200000006 – volume: 19 start-page: 153 year: 2007 ident: 10.1016/j.neucom.2019.03.024_bib0012 article-title: Greedy layer-wise training of deep networks publication-title: Adv. Neural Inf. Process. Syst. – volume: 1 start-page: 1 year: 2009 ident: 10.1016/j.neucom.2019.03.024_bib0028 article-title: A new edge detection method of polarimetric SAR images using the curvelet transform and the Duda operator – volume: 36 start-page: 963 year: 1998 ident: 10.1016/j.neucom.2019.03.024_bib0008 article-title: A three-component scattering model for polari- metric SAR data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.673687 – volume: 10 start-page: 2108 year: 2015 ident: 10.1016/j.neucom.2019.03.024_bib0017 article-title: Single sample face recognition via learning deep supervised autoencoders publication-title: IEEE Trans. Inf. Forens. Secur. doi: 10.1109/TIFS.2015.2446438 – volume: 13 start-page: 1359 year: 2016 ident: 10.1016/j.neucom.2019.03.024_bib0021 article-title: Stacked sparse autoencoder in PolSAR data classification using local spatial information publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2016.2586109 – volume: 55 start-page: 7177 year: 2017 ident: 10.1016/j.neucom.2019.03.024_bib0032 article-title: Complex-valued convolutional neural network and its application in polarimetric SAR image classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2017.2743222 – volume: 31 start-page: 511 year: 1991 ident: 10.1016/j.neucom.2019.03.024_bib0002 article-title: Terrain classification in SAR images using principal components analysis and neural networks publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.214928 – volume: 41 start-page: 2089 year: 2003 ident: 10.1016/j.neucom.2019.03.024_bib0031 article-title: The use of fully polarimetric information for the fuzzy neural classification of SAR images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2003.813494 – volume: 52 start-page: 2197 year: 2014 ident: 10.1016/j.neucom.2019.03.024_bib0006 article-title: Integrating color features in polarimetric SAR image classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2013.2258675 – volume: 45 start-page: 73 year: 2007 ident: 10.1016/j.neucom.2019.03.024_bib0009 article-title: Target scattering decomposition in terms of roll-invariant target param- eters publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2006.886176 – start-page: 2818 year: 2014 ident: 10.1016/j.neucom.2019.03.024_bib0019 article-title: Multilayer feature learning for polarimetric synthetic radar data classification – volume: 10 start-page: 1 year: 2016 ident: 10.1016/j.neucom.2019.03.024_bib0005 article-title: Polarimetric SAR feature extraction with neighborhood preservation-based deep learning publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. – volume: 34 start-page: 498 year: 1996 ident: 10.1016/j.neucom.2019.03.024_bib0010 article-title: A review of target decomposition theorems in radar polarimetry publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.485127 – volume: 9 start-page: 3001 year: 2016 ident: 10.1016/j.neucom.2019.03.024_bib0007 article-title: Semisupervised feature extraction with neighborhood constraints for polarimetric SAR classification publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. doi: 10.1109/JSTARS.2016.2532922 – volume: 5 start-page: 13 year: 2010 ident: 10.1016/j.neucom.2019.03.024_bib0014 article-title: Deep machine learning-a new frontier in artificial intelligence research [research frontier] publication-title: IEEE Comput. Intell. Mag. doi: 10.1109/MCI.2010.938364 – volume: 5 start-page: 668 year: 2008 ident: 10.1016/j.neucom.2019.03.024_bib0020 article-title: Region-based classification of polarimetric SAR images using Wishart MRF publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2008.2002263 |
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| SubjectTerms | Deep learning Feature learning Multi-scale Polsar classification Stacked sparse autoencoder |
| Title | PolSAR image classification based on multi-scale stacked sparse autoencoder |
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