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基于多尺度超像素融合的合成孔径雷达图像目标检测

Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion.

作者信息

Liu Ming, Chen Shichao, Lu Fugang, Xing Mengdao, Wei Jingbiao

机构信息

Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi'an 710062, China.

School of Computer Science, Shaanxi Normal University, Xi'an 710119, China.

出版信息

Sensors (Basel). 2021 Feb 26;21(5):1643. doi: 10.3390/s21051643.

DOI:10.3390/s21051643
PMID:33652908
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7956173/
Abstract

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

摘要

对于合成孔径雷达（SAR）图像复杂场景中的目标检测，陆地区域的虚警难以消除，尤其是靠近海岸线的虚警。针对这一问题，本文提出了一种基于多尺度超像素分割融合的算法。首先，利用不同尺度的超像素分割对SAR图像进行划分。对于每个尺度下的超像素，通过判断其统计特性实现海陆分割。然后，将各尺度下获得的海陆分割结果与恒虚警率（CFAR）检测器的结果相结合，以消除位于SAR图像陆地区域的虚警。最后，为提高所提算法 的鲁棒性，将不同尺度下获得的检测结果融合在一起，实现最终的目标检测。真实SAR图像上的实验结果验证了所提算法的有效性。

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