Ning Wang, Yinghua Wang, Hongwei Liu, Qunsheng Zuo, Jinglu He
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Feature-Fused SAR Target Discrimination Using Multiple Convolutional Neural Networks
Target discrimination has been one of the hottest issues in the interpretation of synthetic aperture radar (SAR) images. However, the presence of speckle noise and the absence of robust features make SAR discrimination difficult to deal with. Recently, convolutional neural network has obtained state-of-the-art results in pattern recognition. In this letter, we propose a target discrimination framework that jointly uses intensity and edge information of SAR images. This framework contains three parts, namely, feature extraction block, feature fusion block, and final classification block. In addition, a novel feature fusion method that can preserve the spatial relationship of different features is introduced. Experimental results on the miniSAR data demonstrate the effectiveness of our method.
期刊介绍:
IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.