基于显著性的SAR图像船舶目标检测方法

doi:10.7523/j.issn.2095-6134.2019.03.014

中国科学院大学学报 ›› 2019, Vol. 36 ›› Issue (3): 401-409.DOI: 10.7523/j.issn.2095-6134.2019.03.014

基于显著性的SAR图像船舶目标检测方法

闫成章^1,2, 刘畅^1,2

1. 中国科学院电子学研究所, 北京 100190;
2. 中国科学院大学, 北京 100049

收稿日期:2017-12-28 修回日期:2018-03-30 发布日期:2019-05-15
通讯作者: 闫成章
基金资助:
国家重点研发计划项目（2017YFB0503001）资助

A ship target detection method of SAR image based on saliency detection

YAN Chengzhang^1,2, LIU Chang^1,2

1. Insititute of Electrics, Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2017-12-28 Revised:2018-03-30 Published:2019-05-15

摘要/Abstract

摘要： 船舶检测是SAR海洋应用的重要方面。提出一种通用的检测方法，用以检测不同状况下的SAR图像船舶目标。首先将SAR图像分解为金字塔图像序列，然后对其中每一层图像使用谱残差法进行显著性检测，得到包含船舶目标的显著性子图；而后融合各子图得到最终显著图，对该显著图应用优化阈值的分割方法得到最终的检测结果。SAR数据实验结果表明，该方法具有复杂度低、检测精度高等特点，且极大降低了对先验知识的依赖。

关键词: SAR图像, 多尺度, 显著性检测

Abstract: Ship detection is an important direction of SAR image application in maritime surveillance. A multi-scale optimization threshold saliency detection is proposed in this study, for detecting ship targets of SAR image.The SAR image is first decomposed into a pyramid image sequence. Then the saliency detection is performed by using the spectral residual method for each layer in the sequence, and the salient subgraphs that contain ship targets are obtained. Finally, the subgraphs are fused and the optimization threshold segmentation method that applies to the saliency map is used to produce the final result. Experimental results show that the proposed approach has better detection performance, and it has low complexity and high detection accuracy and greatly reduces the dependence on prior knowledge.

Key words: SAR image, multi-scale, saliency detection

中图分类号:

TP753

闫成章, 刘畅. 基于显著性的SAR图像船舶目标检测方法[J]. 中国科学院大学学报, 2019, 36(3): 401-409.

YAN Chengzhang, LIU Chang. A ship target detection method of SAR image based on saliency detection[J]. , 2019, 36(3): 401-409.

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基于显著性的SAR图像船舶目标检测方法

A ship target detection method of SAR image based on saliency detection

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