Remote Sensing (Oct 2021)

Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth

  • Zongyong Cui,
  • Yi Qin,
  • Yating Zhong,
  • Zongjie Cao,
  • Haiyi Yang

DOI
https://doi.org/10.3390/rs13214315
Journal volume & issue
Vol. 13, no. 21
p. 4315

Abstract

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In dealing with the problem of target detection in high-resolution Synthetic Aperture Radar (SAR) images, segmenting before detecting is the most commonly used approach. After the image is segmented by the superpixel method, the segmented area is usually a mixture of target and background, but the existing regional feature model does not take this into account, and cannot accurately reflect the features of the SAR image. Therefore, we propose a target detection method based on iterative outliers and recursive saliency depth. At first, we use the conditional entropy to model the features of the superpixel region, which is more in line with the actual SAR image features. Then, through iterative anomaly detection, we achieve effective background selection and detection threshold design. After that, recursing saliency depth is used to enhance the effective outliers and suppress the background false alarm to realize the correction of superpixel saliency value. Finally, the local graph model is used to optimize the detection results. Compared with Constant False Alarm Rate (CFAR) and Weighted Information Entropy (WIE) methods, the results show that our method has better performance and is more in line with the actual situation.

Keywords