Cross-layer hybrid feature aggregation network for change detection in very high-resolution remote sensing images

Very high-resolution remote sensing images are used to detect the changes with finer results. Accompanying with plenty of details, the environment around targets also becomes more complex, which poses challenges for change detection. In recent years, hybrid methods based on transformer and CNN have been widely used. The two methods usually are adopted to extract the change information because they can take advantage of global semantic relations and long-range spatial dependencies at the same time. However, within some complex environments, there is still information loss and inaccurate detection because the features cannot be fully integrated. We proposed a new cross-level hybrid feature aggregation network for change detection to improve the performance of change detection, especially within a complex environment. Within the new network, a parallel hybrid CNN-Transformer structure is adopted to model globally and locally, which extracts the features of different levels and produces rich semantic features. Meanwhile, the multi-branch feature interaction is used to implement interaction and fusion for multiscale feature information. Furthermore, multiscale feature aggregation was applied to remove redundancy. Subsequently, CNN-Transformer change feature enhancement is used to enhance the representation. Compared with several state-of-the-art methods on three available datasets, the accuracy is increased by 0.09%, 1.12%, and 2.62%, respectively. The experiments indicate that the method proposed in this paper detects the changed targets as continuous and complete objects with clear edges. Within a complex environment, it suppresses pseudo-changes and extracts more small changed targets.