Parameter and Data Efficient Framework for Lightweight Change Detection

Despite extensive research in change detection methods for remote sensing, there has been relatively little work on optimizing these techniques for onboard computing scenarios. In this study, we propose a novel two-stage change detection framework designed to address the unique challenges of onboard satellite processing, particularly managing limited resources and mitigating high data volumes transmitted to earth. Our approach consists of a coarse-grained stage, serving as an early exit mechanism to efficiently predetermine the likelihood of change within an image, and a fine-grained stage, aimed at generating a detailed change map. By incorporating this dual-stage methodology, our framework effectively balances the need for rapid, resource-efficient computations and the demand for detailed, accurate change detection while reducing data to be processed and transmitted. We apply our approach across various backbone architectures to demonstrate its efficacy and report enhancements in both accuracy metrics and runtime performance. Our results showcase significant improvements, affirming the potential of our methodology to realize onboard change detection processes in remote sensing applications.