A Difference Enhanced Neural Network for Semantic Change Detection of Remote Sensing Images

Deep learning techniques have been widely used for semantic change detection (SCD) of remote sensing images (RSIs) and have shown encouraging performance. In this letter, we propose a novel neural network by embedding the difference enhancement (DE) module into the adjacent layers of ResNet for SCD of RSIs (DESNet), which can pay more attention to the changes of bitemporal RSIs. Furthermore, we deploy the module of multiscale parallel sampling spatial–spectral nonlocal (SSN) after feature extraction, which can effectively improve the robustness to large-scale changes and the integrity of the changed objects by fusing global features that sampled from the multiscale feature space. The experimental tests demonstrate that our DESNet can achieve state-of-the-art accuracy on the SECOND dataset and the Landsat-SCD dataset.