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      An Aircraft Detection Method Based on Convolutional Neural Networks in High-Resolution SAR Images

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          Abstract

          In the field of image processing using Synthetic Aperture Radar (SAR), aircraft detection is a challenging task. Conventional approaches always extract targets from the background of an image using image segmentation methods. Nevertheless, these methods mainly focus on pixel contrast and neglect the integrity of the target, which leads to locating the object inaccurately. In this study, we build a novel SAR aircraft detection framework. Compared to traditional methods, an improved saliency-based method is proposed to locate candidates coarsely and quickly in large scenes. This proposed method is verified to be more efficient compared with the sliding window method. Next, we design a convolutional neural network fitting in SAR images to accurately identify the candidates and obtain the final detection result. Moreover, to overcome the problem of limited available SAR data, we propose four data augmentation methods comprising translation, speckle noising, contrast enhancement, and small-angle rotation. Experimental results show that our framework achieves excellent performance on the high-resolution TerraSAR-X dataset.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 April 2017
          : 6
          : 2
          : 195-203
          Affiliations
          [1 ] ①(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China) ②(University of Chinese Academy of Science, Beijing 100049, China)
          [2 ] ①(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)
          Article
          dfaf6ed8969e4f23bcee4bf6d65d43b8
          10.12000/JR17009

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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