Edge Enhanced Channel Attention-Based Graph Convolution Network for Scene Classification of Complex Landscapes

Monitoring the land covers in complex landscapes is of great significance for the sustainable development of mine geo-environments. As most existing remote sensing scene datasets are composed of RGB images, there is a lack of multimodal datasets for complex landscapes with mining land covers (MLCs) at a fine-scale. In this study, a new dataset was created by the China University of Geosciences (CUG), Wuhan (named CUG-MLCs) using ZiYuan-3 imagery-based multispectral and topographic data. Moreover, the characteristics of multisize objects, irregular or blurred edges, and spectral-spatial-topographic heterogeneity and variability limited the classification accuracy. Therefore, an edge enhanced channel attention-based graph convolution network (ECA-GCN) was proposed and tested. The proposed ECA-GCN includes three key modules. 1) Multiscale and shallow feature fusion, used to fuse the multiscale convolutional features and shallow features, which helps present the MLC features with various scales; 2) edge enhanced channel attention, used to further select effective channels after a spatial edge feature enhancement, which helps identify irregular or blurred MLCs; and 3) edge detection-based GCN, used for edge feature-based adjacency matrix and feature maps from (2) to construct GCN, which can obtain edge node relation and global contextual information. This framework improved the representation of complex landscape characteristics. The proposed ECA-GCN achieved an overall accuracy of 66.60% +/- 1.39%, averaged accuracy of 36.25% +/- 1.50%, and Kappa of 55.91% +/- 2.05%, thus, outperforming other models. In general, the proposed dataset and model were positive for the fine classification of complex landscapes.