面向变化检测的SAR图像超像素协同分割算法  

Change detection-oriented superpixel cosegmentation algorithm for SAR images

作　　者：邵宁远 邹焕新[1] 陈诚[1] 李美霖 秦先祥[2] SHAO Ningyuan;ZOU Huanxin;CHEN Cheng;LI Meilin;QIN Xianxiang(College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China;Information and Navigation College, Air Force Engineering University, Xi’an 710077, China)

机构地区：[1]国防科技大学电子科学学院,湖南长沙410073 [2]空军工程大学信息与导航学院,陕西西安710077

出　　处：《系统工程与电子技术》2019年第7期1496-1503,共8页Systems Engineering and Electronics

基　　金：国家自然科学基金(61331015,41601436)资助课题

摘　　要：针对面向对象的合成孔径雷达(synthetic aperture radar, SAR)图像变化检测中存在的多时相图像边界和空间对应关系不一致的问题,提出了一种面向变化检测的SAR图像超像素协同分割算法。首先,分别计算两幅不同时相SAR图像中两个像素点之间的强度相似度,并进行加权组合得到新的像素强度相似度。其次,对两幅不同时相的SAR图像及其对数比值图分别进行边缘提取,以同一像素位置的最大边缘值构造二值边缘图。最后,以融合了像素强度、空间距离和边缘信息的相似度代替CIELAB彩色空间相似度,利用改进简单线性迭代聚类算法对多时相SAR图像进行超像素分割,得到边界准确、空间对应的协同分割结果。基于一组仿真和一组实测多时相SAR图像的协同分割实验结果表明,该方法的边缘贴合率、欠分割误差和可达分割准确率均优于其他4种经典方法。Due to the inconsistency of multitemporal images’ boundaries and spatial correspondence in the task of object-based synthetic aperture radar (SAR) image change detection, a superpixel cosegmentation for SAR image change detection is proposed. Firstly, the pixel intensity similarities between the two pixels of the multitemporal SAR images are calculated respectively, which are then combined using a weight factor to form a new similarity measurement. Additionally, the edge magnitudes of the two multitemporal SAR images as well as their log-ratio image are detected , and the maximum value among which is chosen to form a binary edge map image. Finally, the weighted similarity based on pixel intensity, location distance and edge information is used to replace the CIELAB space similarity for local clustering in simple linear iterative clustering. The multitemporal SAR images are then cosegmented with accurate boundaries and spatial correspondence. The experimental results conducted on a pair of simulated SAR images and a pair of real-world multitemporal SAR images demonstrate that the boundary recall, undersegmentation error and achievable segmentation accuracy of the proposed method are better than those of other four state-of-the-art methods.

关 键 词：合成孔径雷达 超像素分割 图像协同分割 变化检测 

分 类 号：TN957[电子电信—信号与信息处理]