基于SNIC的双时相SAR图像超像素协同分割算法

针对面向区域的合成孔径雷达(synthetic aperture radar, SAR)图像变化检测方法中存在的双时相图像边缘和空间对应关系不一致的问题, 提出了一种基于简单非迭代聚类(simple non-iterative clustering, SNIC)的双时相SAR图像超像素协同分割算法。首先, 构造一幅包含双时相SAR图像特征的融合图像, 计算待处理像素点到聚类中心的像素强度相似度和空间距离相似度。其次, 采用一种高效的多尺度弱边缘检测算法, 对双时相SAR图像分别进行边缘检测并融合边缘检测结果。最后, 将像素强度相似度、空间距离相似度和边缘信息进行加权以替代原始SNIC算法中的距离测度, 实现对SAR融合图像的超像素分割, 得到与双时相SAR图像中真实地物边缘均贴合的协同分割结果。基于一组仿真和一组实测双时相SAR图像的超像素协同分割实验结果表明, 该算法的边缘贴合率、欠分割误差和可达分割准确率均优于其他7种经典方法。

Super pixel cooperative segmentation algorithm for bi-temporal SAR image based on SNIC

Aiming at the problem of the inconsistency of bi-temporal images’boundaries and spatial correspondence in the task of region-based synthetic aperture radar(SAR)image change detection,a superpixel cosegmentation algorithm based on simple non-iterative clustering(SNIC)for bi-temporal SAR images is proposed.First,a fused image containing the features of the bi-temporal SAR images is constructed,and the pixel intensity similarity and spatial distance similarity between the pixels to be processed and the cluster center is calculated.Second,a computationally efficient multiscale edge detection algorithm is adopted and used to detect the edges of the bi-temporal SAR images respectively,and the edge detection results are fused to form an edge map.Finally,the pixel intensity similarity,spatial distance similarity and edge map information are weighted to replace the distance measure in the original SNIC algorithm and the improved SNIC is utilized to perform superpixel segmentation on the fused image to obtain the segmentation result which fits the real terrain edges in the bi-temporal SAR images.The experimental results conduct on a pair of simulated SAR images and a pair of real-world bi-temporal SAR images demonstrate that the boundary recall,under-segmentation error and achievable segmentation accuracy of the proposed method are better than those of other seven state-of-the-art methods.