Multitemporal SAR Images Change Detection Considering Ambiguous Co-Registration Errors: A Unified Framework
Jingxing Zhu, Guangyao Zhou, Feng Wang, Rui Liu, Yuming Xiang, Wenzhi Wang, Hongjian You
Abstract
Image registration and change detection are two important tasks in synthetic aperture radar (SAR) image processing. Conventional researches considered them as two individual problems, where change detection requires pixel-wise registration first. However, they can be integrated into one processing framework and benefit from each other. This letter proposes a unified algorithm to address the change detection problem in multitemporal SAR images considering ambiguous co-registration errors. Based on the framework of optical flow, we designed an advanced objective function by introducing three individual features of heterogeneous, homogeneous, and changed areas in multitemporal SAR images. Afterward, the objective function is iteratively optimized, resulting in pixel-wise correspondences and a changed map. Experimental results on multitemporal SAR images show that the proposed algorithm achieves good performances both on registration accuracy and change detection discrimination.