Semantic Segmentation for High-Resolution Remote-Sensing Images via Dynamic Graph Context Reasoning

Abstract

Semantic segmentation for high-resolution remote-sensing (HRRS) images is one of the most challenging tasks in remote-sensing images understanding. Capturing long-range dependencies in feature representations is crucial for semantic segmentation. Recent graph-based global reasoning networks (<i>GloRe</i>) focus on modeling the global contextual relationship between latent nodes based on fully connected graph in interaction space. However, such a dense operation is susceptible to redundant features. Most importantly, it treats each node equally, ignoring the contextual relationship between nodes in graphs. In this work, we propose to explore more effective contextual representations in semantic segmentation by introducing dynamic graph contextual reasoning module over <i>GloRe</i>, dubbed DGCR. It incorporates local semantic information that represents the relationships between nodes to perform long-range contextual reasoning. More specifically, to provide effectively and flexible reasoning in graph-based reasoning approaches, we construct <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbor (KNN) graphs rather than fully connected graphs using only the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> closest nodes depends on pairwise semantic distance. Extensive experiments on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam datasets demonstrate the effectiveness and superiority of our proposed DGCR module over other state-of-the-art methods.