WRICNet: A Weighted Rich-Scale Inception Coder Network for Remote Sensing Image Change Detection

Yu Jiang, Lei Hu, Yongmei Zhang, Xin Yang

Abstract: The majority of remote sensing image change detection models focus on extracting high-level semantic features. However, it is difficult to detect the changing area with large differences in size simultaneously, and the accurate changing area edge is difficult. To solve these problems, we propose a weighted rich-scale inception coder network (WRICNet), consisting of the weighted rich-scale inception module and the weighted rich-scale coder module. The former can retain the low-level multiscale feature (LMF), and the latter can extract the high-level multiscale feature (HMF). By fusing LMF and HMF, both large and small changing areas can be detected and make changing area edge accurate. To complement LMF and HMF well, we propose a weighted scale block, which assigns appropriate weights to multiscale features. Compared to comparative methods, performance experiments on datasets demonstrate that our proposed method can further reduce false alarms and miss alarms and make the changing area edge more accurate. Furthermore, ablation studies show that our training strategy, model settings, and improvements of the inception and rich-scale blocks are effective.