Scene Changes Understanding Framework Based on Graph Convolutional Networks and Swin Transformer Blocks for Monitoring LCLU Using High-Resolution Remote Sensing Images

Abstract

High-resolution remote sensing images with rich land surface structure can provide data support for accurately understanding more detailed change information of land cover and land use (LCLU) at different times. In this study, we present a novel scene change understanding framework for remote sensing which includes scene classification and change detection. To enhance the feature representation of images in scene classification, a robust label semantic relation learning (LSRL) network based on EfficientNet is presented for scene classification. It consists of a semantic relation learning module based on graph convolutional networks and a joint expression learning framework based on similarity. Since the bi-temporal remote sensing image pairs include spectral information in both temporal and spatial dimensions, land cover and land use change monitoring can be improved by using the relationship between different spatial and temporal locations. Therefore, a change detection method based on swin transformer blocks (STB-CD) is presented to obtain contextual relationships between targets. The experimental results on the LEVIR-CD, NWPU-RESISC45, and AID datasets demonstrate the superiority of LSRL and STB-CD over other state-of-the-art methods.