Classification of SAR Images Based on Deep Deconvolutional Network

WANG Yunyan; HE Chu; ZHAO Shouneng; CHEN Dong; LIAO Mingsheng

doi:10.13203/j.whugis20140366

Volume 40 Issue 10

Oct. 2015

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Geomatics and Information Science of Wuhan University > 2015 > 40(10): 1371-1376. > DOI: 10.13203/j.whugis20140366

WANG Yunyan, HE Chu, ZHAO Shouneng, CHEN Dong, LIAO Mingsheng. Classification of SAR Images Based on Deep Deconvolutional Network[J]. Geomatics and Information Science of Wuhan University, 2015, 40(10): 1371-1376. DOI: 10.13203/j.whugis20140366

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Classification of SAR Images Based on Deep Deconvolutional Network

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2.
School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China;
3.
Electronic and Information School, Wuhan University, Wuhan 430072, China;
4.
ATR Key Laboratory, National University of Defense Technology, Changsha 410073, China

Funds: The National Key Basic Research and Development Program(973 Program) of China, No.2013CB733404;the National Natural Science Foundation of China, Nos.41371342, 61331016; the Natural Science Foundation of Hubei Province.

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Received Date: May 06, 2014
Published Date: October 04, 2015

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Abstract

Abstract

Aim at the problem that the traditional feature extraction methods cannot get the high level structure features, this paper put forward a new soft probability pooling method, which is used in multilayer Deconvolutional Network, then high level structure features can be learned and be used for classification of SAR image. Firstly, the SAR image was divided into patches; then, the feature coding of each patch was obtained by means of multilayer Deconvolutional Networks, which can learn features suitable for image classification in different scale ; finally, the SAR image was classified through the features used in SVM classifier. Experimental results on the first batch domestic PolSAR images show that the classification accuracy rate of the proposed algorithm is superior.
- synthetic aperture radar,
- multilayer learning,
- deconvolutional network,
- image classification,
- soft probability pooling

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