基于邻域显著性的可见光和SAR遥感图像海面舰船协同检测方法  

Cooperative Detection of Ships in Optical and SAR Remote Sensing Images Based on Neighborhood Saliency

作　　者：张强 王志豪 王学谦 李刚[1] 黄立威[2] 宋慧娜 宋朝晖 ZHANG Qiang;WANG Zhihao;WANG Xueqian;LI Gang;HUANG Liwei;SONG Huina;SONG Zhaohui(Department of Electronic Engineering,Tsinghua University,Beijing 100084,China;Beijing Institute of Remote Sensing,Beijing 100854,China;Zhejiang Key Laboratory of Space Information Sensing and Transmission,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区：[1]清华大学电子工程系,北京100084 [2]北京市遥感信息研究所,北京100854 [3]杭州电子科技大学浙江省空间信息感知与传输重点实验室,杭州310018

出　　处：《雷达学报（中英文）》2024年第4期885-903,共19页Journal of Radars

基　　金：国家重点研发计划(2021YFA0715201);国家自然科学基金(62101303,62341130);清华大学电子工程系自主科研计划。

摘　　要：在遥感图像舰船检测任务中,可见光图像细节和纹理信息丰富,但成像质量易受云雾干扰,合成孔径雷达(SAR)图像具有全天时和全天候的特点,但图像质量易受复杂海杂波影响。结合可见光和SAR图像优势的协同检测方法可以提高舰船目标的检测性能。针对在前后时相图像中,舰船目标在极小邻域范围内发生轻微偏移的场景,该文提出一种基于邻域显著性的可见光和SAR多源异质遥感图像舰船协同检测方法。首先,通过可见光和SAR的协同海陆分割降低陆地区域的干扰,并通过RetinaNet和YOLOv5s分别进行可见光和SAR图像的单源目标初步检测;其次,提出了基于单源检测结果对遥感图像邻域开窗进行邻域显著性目标二次检测的多源协同舰船目标检测策略,实现可见光和SAR异质图像的优势互补,减少舰船目标漏检、虚警以提升检测性能。在2022年烟台地区拍摄的可见光和SAR遥感图像数据上,该方法的检测精度AP50相比现有舰船检测方法提升了1.9%以上,验证了所提方法的有效性和先进性。In ship detection through remote sensing images,optical images often provide rich details and texture information;however,the quality of such optical images can be affected by cloud and fog interferences.In contrast,Synthetic Aperture Radar(SAR)provides all-weather and all-day imaging capabilities;however,SAR images are susceptible to interference from complex sea clutter.Cooperative ship detection combining the advantages of optical and SAR images can enhance the detection performance of ships.In this paper,by focusing on the slight shift of ships in a small neighborhood range in the prior and later temporal images,we propose a method for cooperative ship detection based on neighborhood saliency in multisource heterogeneous remote sensing images,including optical and SAR data.Initially,a sea-land segmentation algorithm of optical and SAR images is applied to reduce interference from land regions.Next,single-source ship detection from optical and SAR images is performed using the RetinaNet and YOLOv5s models,respectively.Then,we introduce a multisource cooperative ship target detection strategy based on the neighborhood window opening of single-source detection results in remote sensing images and secondary detection of neighborhood salient ships.This strategy further leverages the complementary advantages of both optical and SAR heterogeneous images,reducing the possibility of missing ship and false alarms to improve overall detection performance.The performance of the proposed method has been validated using optical and SAR remote sensing data measured from Yantai,China,in 2022.Compared with existing ship detection methods,our method improves detection accuracy AP50 by≥1.9%,demonstrating its effectiveness and superiority.

关 键 词：可见光遥感 合成孔径雷达 舰船目标 多源协同 邻域显著性 

分 类 号：TN957.52[电子电信—信号与信息处理]