Semantic Change Detection of Bitemporal Remote Sensing Images Using Frequency Feature Enhancement

Deep learning is a powerful technique for semantic change detection (SCD) of bitemporal remote sensing images. In this work, we propose to improve SCD accuracy using deep learning with frequency feature enhancement (FFE). Specifically, we develop an FFE module that aims to enhance the performance of both binary change detection (BCD) and semantic segmentation, two main key components for obtaining high SCD accuracy, by integrating the Fourier transform and attention mechanisms. Experimental results on the SECOND and LandSat-SCD datasets demonstrate the effectiveness of the proposed method, and it achieves high resolution for change boundaries.