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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| Published in: | Neurocomputing (Amsterdam) Vol. 351; pp. 167 - 179 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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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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| 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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