Mitigating Style Differences in Bitemporal Remote Sensing Images for Change Detection

Change detection has seen significant advancements with the development of deep learning. However, due to variations in sensors or atmospheric conditions, bitemporal images often exhibit visually significant style differences, posing challenges for the detection of changed regions. This paper presents a change detection network designed to effectively address the challenges posed by style differences in bitemporal images. The proposed network comprises a color difference unification module and a generalized feature extraction module, which focuses the network on really changed areas. The color difference unification module harmonizes the color space of bitemporal remote sensing images, thereby mitigating the impact of style differences attributed to objective conditions. The generalized feature extraction module, ensuring robust feature representation for image pairs and further reducing style differences between bitemporal images. Experimental results demonstrate the superiority of our proposed method compared to existing change detection algorithms, confirming its suitability for fulfilling the requirements of change detection tasks.