Dynamic Anomaly Detection of Space Targets From Sequential ISAR With Spatio-Temporal Graph Convolutional Networks

Dynamic anomaly detection of space targets is essential for space situational awareness. With the intrinsic Range Doppler imaging mechanism, the spatio-temporal feature of ISAR sequences has a great potential in dynamic representation. To address this, we propose a novel dynamic anomaly detection method using Spatio-Temporal Graph Convolutional Networks (STGCN) to capture spatio-temporal features from inverse synthetic aperture radar (ISAR) image sequences. By constructing a skeleton model according to the universal geometry of space satellites, our method captures node correlations in both temporal and spatial dimensions from continuous ISAR frames, thereby enabling reliable dynamic target recognition. The effectiveness and superiority of this approach are demonstrated through comparative experiments.