ESR-DMNet: Enhanced Super-Resolution-Based Dual-Path Metric Change Detection Network for Remote Sensing Images With Different Resolutions
Xi Li, Li Yan, Yi Zhang, Huaien Zeng · IEEE Transactions on Geoscience and Remote Sensing · 2024
Remote sensing change detection has always been one of the research hot issues in remote sensing. Current research focuses on studying deep learning change detection methods for remote sensing images with the same resolution. With the prevalence of multi-resolution remote sensing images, how to effectively utilize remote sensing images with different resolutions for change detection is a key issue. To solve this problem, this paper proposes an enhanced super-resolution-based dual-path metric change detection network (ESR-DMNet) to realize high-accuracy and high-efficiency end-to-end change detection of remote sensing images with different resolutions. ESR-DMNet provides a new enhanced super-resolution module for the change detection of remote sensing images with different resolutions, which can perceptively reconstruct low-resolution images into more realistic high-resolution images. ESR-DMNet proposes an effective and efficient dual-path metric change detection network, which processes shallow spatial details information and deep semantic information separately to achieve high-accuracy and high-efficiency change detection. Compared with nine state-of-the-art methods, our method shows good performance at three resolutions on three datasets, SYSU, CDD and CLCD, confirming its potential for change detection tasks in remote sensing images with different resolutions.