Remote sensing image recognition based on generalized regularized auto-encoders
YANG Qianwen, SUN Fuchun
State Key Lab of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Abstract：This paper addresses the problem of sample scarcity in SAR (synthetic aperture radar) images, which are very useful remote sensing sources but are very costly to obtain. SAR images have limited samples which makes it hard to get a standardized “good” model for even simple classification tasks. Then, the limited samples are expanded via generalization of the sample space using prior and a priori information including labels. Then, auto-learning models, regularized auto-encoders, are used to extract useful features from the generalized sample space. A regularization term is used to penalize generalized samples in an image recognition algorithm. The method produces good results on limit-sized subsets from the MNIST database and the SAR MSTAR image dataset. With limited input samples, the method gives better reconstruction errors and recognition accuracies in the SAR ship images than the state-of-art methods. Thus, this algorithm is shown to be successful in both theoretical analyses and tests.
杨倩文, 孙富春. 基于泛化空间正则自动编码器的遥感图像识别[J]. 清华大学学报（自然科学版）, 2018, 58(2): 113-121.
YANG Qianwen, SUN Fuchun. Remote sensing image recognition based on generalized regularized auto-encoders. Journal of Tsinghua University(Science and Technology), 2018, 58(2): 113-121.
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2018, Vol. 58 
