MULTI-LABEL REMOTE SENSING IMAGE CLASSIFICATION WITH DEFORMABLE CONVOLUTIONS AND GRAPH NEURAL NETWORKS

Multi-label remote sensing image classification is a significant yet difficult task due to intra-class variations and label dependencies among land-cover classes. In this paper, we propose a novel multi-label classification model based on deformable convolutions and graph neural networks. Specifically, we first use deformable convolutions to learn image features with geometric transformation invariance and adaptive receptive field. Then we adopt attention mechanism to extract label-related image features. After that, a directed graph is constructed to model the label dependencies, and the label-related features are fused through graph propagation mechanisms. Experiments on UC-Merced and DOTA data sets demonstrate its effectiveness.