High-Resolution SAR Image Classification via Deep Convolutional Autoencoders

Synthetic aperture radar (SAR) image classification is a hot topic in the interpretation of SAR images. However, the absence of effective feature representation and the presence of speckle noise in SAR images make classification difficult to handle. In order to overcome these problems, a deep convol...

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Published in:	IEEE geoscience and remote sensing letters Vol. 12; no. 11; pp. 2351 - 2355
Main Authors:	Geng, Jie, Fan, Jianchao, Wang, Hongyu, Ma, Xiaorui, Li, Baoming, Chen, Fuliang
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.11.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Classification Deep convolutional autoencoders (DCAEs) Feature extraction Image classification Networks Noise Representations Speckle supervised classification Synthetic aperture radar synthetic aperture radar (SAR) image Texture Training Transformations Transforms supervised classification synthetic aperture radar (SAR) image Deep convolutional autoencoders (DCAEs)
ISSN:	1545-598X, 1558-0571
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Abstract	Synthetic aperture radar (SAR) image classification is a hot topic in the interpretation of SAR images. However, the absence of effective feature representation and the presence of speckle noise in SAR images make classification difficult to handle. In order to overcome these problems, a deep convolutional autoencoder (DCAE) is proposed to extract features and conduct classification automatically. The deep network is composed of eight layers: a convolutional layer to extract texture features, a scale transformation layer to aggregate neighbor information, four layers based on sparse autoencoders to optimize features and classify, and last two layers for postprocessing. Compared with hand-crafted features, the DCAE network provides an automatic method to learn discriminative features from the image. A series of filters is designed as convolutional units to comprise the gray-level cooccurrence matrix and Gabor features together. Scale transformation is conducted to reduce the influence of the noise, which integrates the correlated neighbor pixels. Sparse autoencoders seek better representation of features to match the classifier, since training labels are added to fine-tune the parameters of the networks. Morphological smoothing removes the isolated points of the classification map. The whole network is designed ingeniously, and each part has a contribution to the classification accuracy. The experiments of TerraSAR-X image demonstrate that the DCAE network can extract efficient features and perform better classification result compared with some related algorithms.
AbstractList	Synthetic aperture radar (SAR) image classification is a hot topic in the interpretation of SAR images. However, the absence of effective feature representation and the presence of speckle noise in SAR images make classification difficult to handle. In order to overcome these problems, a deep convolutional autoencoder (DCAE) is proposed to extract features and conduct classification automatically. The deep network is composed of eight layers: a convolutional layer to extract texture features, a scale transformation layer to aggregate neighbor information, four layers based on sparse autoencoders to optimize features and classify, and last two layers for postprocessing. Compared with hand-crafted features, the DCAE network provides an automatic method to learn discriminative features from the image. A series of filters is designed as convolutional units to comprise the gray-level cooccurrence matrix and Gabor features together. Scale transformation is conducted to reduce the influence of the noise, which integrates the correlated neighbor pixels. Sparse autoencoders seek better representation of features to match the classifier, since training labels are added to fine-tune the parameters of the networks. Morphological smoothing removes the isolated points of the classification map. The whole network is designed ingeniously, and each part has a contribution to the classification accuracy. The experiments of TerraSAR-X image demonstrate that the DCAE network can extract efficient features and perform better classification result compared with some related algorithms.
Author	Li, Baoming Chen, Fuliang Fan, Jianchao Wang, Hongyu Ma, Xiaorui Geng, Jie
Author_xml	– sequence: 1 givenname: Jie surname: Geng fullname: Geng, Jie email: gengjie@mail.dlut.edu.cn organization: Sch. of Inf. & Commun. Eng., Dalian Univ. of Technol., Dalian, China – sequence: 2 givenname: Jianchao surname: Fan fullname: Fan, Jianchao email: fjchaonmemc@163.com organization: Sch. of Control Sci. & Eng., Dalian Univ. of Technol., Dalian, China – sequence: 3 givenname: Hongyu surname: Wang fullname: Wang, Hongyu email: whyu@dlut.edu.cn organization: Sch. of Inf. & Commun. Eng., Dalian Univ. of Technol., Dalian, China – sequence: 4 givenname: Xiaorui surname: Ma fullname: Ma, Xiaorui email: xrui.ma@gmail.com organization: Sch. of Inf. & Commun. Eng., Dalian Univ. of Technol., Dalian, China – sequence: 5 givenname: Baoming surname: Li fullname: Li, Baoming email: libaoming@cast.cn organization: Space Star Technol. Co., Ltd., Beijing, China – sequence: 6 givenname: Fuliang surname: Chen fullname: Chen, Fuliang email: cfl0410@126.com organization: Space Star Technol. Co., Ltd., Beijing, China
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Cites_doi	10.1109/CVPR.2014.244 10.1145/1961189.1961199 10.1109/LGRS.2013.2287295 10.1109/TGRS.2008.2002881 10.1109/TGRS.2013.2265413 10.1109/LGRS.2014.2386351 10.1080/07055900.2001.9649675 10.1109/TPAMI.2008.79 10.1016/j.jag.2014.09.019 10.1109/CVPR.2014.243 10.1561/2200000006 10.1109/TGRS.2014.2357078 10.1109/TIP.2013.2259833 10.1162/neco.2006.18.7.1527 10.1016/j.jvcir.2014.03.009
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References	ref13 ref12 lee (ref1) 2009; 47 ref11 ref10 xie (ref15) 0 ref2 ref17 ref16 srivastava (ref18) 2014; 15 he (ref3) 2015; 12 ref8 ref7 ref9 vincent (ref14) 2010; 11 ref4 ref6 ref5
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SubjectTerms	Accuracy Classification Deep convolutional autoencoders (DCAEs) Feature extraction Image classification Networks Noise Representations Speckle supervised classification Synthetic aperture radar synthetic aperture radar (SAR) image Texture Training Transformations Transforms
Title	High-Resolution SAR Image Classification via Deep Convolutional Autoencoders
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