Cross-scale Heterogeneous Convolution Change Detection Based on Spatial-Spectral Information Fusion for Remote Sensing Imagery

Change Detection in remote sensing images typically aims to accurately determine any significant land surface changes, based on acquired multi-temporal image data, being a pivotal task in remote sensing image processing. Recently, deep learning has been widely applied in machine vision with remarkable potential demonstrated for performing change detection in images. Current multi-scale feature fusion methods, while enhancing algorithm performance, often introduce a significant number of redundant parameters, thereby increasing model complexity. This paper presents a novel approach to addressing this challenge. A cross-scale heterogeneous convolution change detection method is proposed. It strengthens the perception of multi-scale information without adding excessive redundant parameters, mitigating the detail-blurring issues caused by fusion. By integrating scale perception with spatial-spectral information aggregation, the proposed approach effectively alleviates scale sensitivity issues, improving change detection performance in complex multi-scale environments. This is showcased by comparative experiments on a challenging real-world dataset with seven existing high-performing methods, the proposed method achieved an F1-score of 84.15% and an IoU of 72.64%, demonstrating the advancement of the proposed method.