Sparse Robust Filters for scene classification of Synthetic Aperture Radar (SAR) images

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• 作者：Shuyuan YangAuthor Vitae ; Min Wang ; ^{wangmin@xidian.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Hezhao LongAuthor Vitae ; Zhi LiuAuthor Vitae
• 关键词：Scene classification ; Synthetic Aperture Radar ; Sparse Robust Filters ; Hierarchical group sparse coding
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 April 2016
• 年：2016
• 卷：184
• 期：Complete
• 页码：91-98
• 全文大小：1892 K

文摘

With the increasing resolution of Synthetic Aperture Radar (SAR) images, extracting their discriminative features for scenes classification has become a challenging task, because SAR images are very sensitive to target aspect brought by shadowing effects, interaction of the signature with the environment, and so on. Moreover, SAR images are remarkably polluted by the multiplicative speckle noise, which makes the conventional feature extractors inefficient. In this paper we advance new Sparse Robust Filters (SRFs) for automatic learning of discriminant features of scenes. A Hierarchical Group Sparse Coding (HGSC) model is proposed to learn a set of sparse and robust filters, to capture the multiscale local descriptors that are robust to noises. Some experiments are taken on a TerraSAR-X images dataset (in the middle of the Swabian Jura, the Nördlinger Ries, HH, observed on July, 2007), and a Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, to evaluate the performance of our proposed method. The experimental results show that our method can achieve higher classification accuracy compared with other related approaches.