Airplane State Discrimination From Single-Temporal High-Resolution Remote Sensing Images

Abstract

The absence of temporal information in single-temporal satellite remote sensing images presents a substantial challenge for target state discrimination. In this letter, a pioneering Remote Sensing Airplane State Discrimination Network (RSASDNet) is introduced, by leveraging the relationship between targets and their backgrounds in single-temporal high-resolution remote sensing images. To facilitate the study, we take airplane state discrimination as an example, and a Remote Sensing Airport Panoptic Segmentation with Airplane States Dataset (RSAPS-ASD) is constructed. RSASDNet incorporates two key innovations: 1) a scene knowledge graph generation module that constructs scene knowledge representation by capturing spatial relationships between airplane instances and their surrounding environment (e.g., taxiways and hangars); and 2) a novel graph-image hybrid convolution discrimination module that synergistically integrates structural knowledge and spatial semantic information through dedicated dual-branch learning. The effectiveness of the proposed method is validated using RSAPS-ASD, with experimental results demonstrating that RSASDNet achieves an impressive accuracy of 73.95% in airplane state discrimination.