一种改进的子视图交叉相关法检测海面小型移动船舶  

An Improved Algorithm for Small-moving Ship Detection Based on the Correlation Between Sub-aperture Images

作　　者：王海鹏[1] 金亚秋[1] 大内和夫[2] 

机构地区：[1]复旦大学,波散射与遥感信息教育部重点实验室,上海200433 [2]日本防卫大学,神奈川239-6868

出　　处：《遥感信息》2008年第3期35-38,共4页Remote Sensing Information

基　　金：国家自然科学资金项目(批准号:40637033,60571050)

摘　　要：本文基于船舶检测的子视图交叉相关法,提出一种SAR图像检测小型移动船舶的方法,突破了原算法仅适用于检测大型或静止目标的限制。SAR图像数据由ALOS-PALSAR采集,海面实况已知数据包括ALOS经过时的海面船只数量、航速及航向等参数。实验结果表明本文提出方法优于恒虚警率CFAR与原有的子视图相关法,同时也讨论了用ALOS-PALSAR图像检测船舶时,其尺寸大小对识别船舶的限制。Based on the correlation of sub-look images, this paper presents an approach of detecting small and moving ships. The shift between sub-aperture images caused by ship movement makes detection more difficult. In this paper, the improved approach eliminates this effect. SAR data used in this paper is obtained by ALOS-PALSAR data at HH polarization, high resolution mode. The study area is over Tosa bay in Kochi, Japan. There are three fishing ships when the image is obtained. CFAR is also studied in this paper, for small-scale wave condition, Gaussian CFAR is suitable for target detection. The results show that CFAR is not good for detecting small ships, which shows little difference with background in SAR image. Moreover, the correlation between sub-look images method cannot obtain good result. The improved approach is adopted by eliminating the shift caused by ship movement. The result shows that the improved approach is effective for detecting small-moving targets.

关 键 词：SAR 多视处理 船舶检测 ALOS-PALSAR 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]