High-Low Frequency Reduction Model for Real-Time Change Detection and Coregistration in Location Discrepancy Sensing Images

Xingliang Xu, Chung-Ming Own, Xiantuo Lou, Zheng Wang, Wenyuan Tao

Abstract: Change detection and coregistration problems, essential tasks in remote sensing image processing, can identify the differences in images of the same scene (e.g., buildings or forests) captured at two distinct time points. These images may be detected by the same or different sensors. Recently, new methods have been developed for such detection tasks, including fully convolutional networks and attention mechanisms, with favorable results. However, in fully convolutional networks, some useless information may be extracted from the multidimensional information of an image. Real-time processing is difficult because of location discrepancies, the large scale of images, and the fusion cropping problem; thus, change detection may be time-consuming and require a substantial amount of hardware resources. In our study, a novel network model was designed to overcome these problems by using high- and low-frequency image information for feature extraction. By using similarity and homography measurements, our proposed system solved the change detection and coregistration problems efficiently. A batch change detection dataset and an established discrepancy dataset were used for evaluation. The results revealed that the proposed method achieved high accuracy in detecting changes in real-time.