Volume 8 Issue 3
Jun.  2019
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Article Navigation >  Journal of Radars  > 2019  >  8(3): 400-412
ZHANG Jinsong, XING Mengdao, and SUN Guangcai. A water segmentation algorithm for SAR image based on dense depthwise separable convolution[J]. Journal of Radars, 2019, 8(3): 400–412. doi: 10.12000/JR19008
Citation: ZHANG Jinsong, XING Mengdao, and SUN Guangcai. A water segmentation algorithm for SAR image based on dense depthwise separable convolution[J]. Journal of Radars, 2019, 8(3): 400–412. doi: 10.12000/JR19008
shu

A Water Segmentation Algorithm for SAR Image Based on Dense Depthwise Separable Convolution

doi: 10.12000/JR19008

  • National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

  • Collaborative Innovation Center of Information Sensing and Understand, Xidian University, Xi’an 710071, China

Funds:  The State Key Research Development Program (2017YFC1405600), The Foundation for Innovative Research Groups of the National Natural Science Foundation of China (61621005)
More Information
  • Corresponding author: ZHANG Jinsong, jinsongxd@163.com
  • Received Date: 2019-01-14
  • Rev Recd Date: 2019-04-08
    • Available Online: 2019-06-19
  • Publish Date: 2019-06-01
    Abstract
  • Water segmentation of real SAR images is of great significance in military and civilian applications such as ship target detection and disaster monitoring. To solve the issues of poor robustness and inaccurate segmentation of traditional water segmentation algorithms, this paper first establishes a SAR water segmentation dataset based on the GF3 satellite and then presents a segmentation network architecture based on depthwise separable convolution. The network takes real SAR images as inputs, extracts high-dimensional features through depthwise separable and dilated convolutions, constructs an up-sampling and decoding module based on bilinear interpolation, and then outputs the corresponding segmentation results. The segmentation results of a water segmentation dataset show that the proposed segmentation method remarkably improves the segmentation accuracy, the segmentation robustness and running speed than traditional method. Therefore, the findings demonstrate the excellent practical engineering value of the proposed algorithm.

     

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