Explainable Shape Anomaly Detection of Space Targets from ISAR Image Sequences

Shape anomaly detection of satellites is critical to ensuring their safe operation. With the intrinsic range-Doppler projection mechanism, the inverse synthetic aperture radar (ISAR) image sequence has a high potential for localizing and detecting satellites’ shape anomalies. In this manuscript, we propose a Fully Convolutional Data Description (FCDD) joint temporal sequential classification network to extract both spatial and temporal information for shape anomaly detection of space targets. The explainable FCDD network is initially built to generate explainable heatmaps of anomalies. An attention-based GRU is used to learn context information between heatmap sequences by converting detection into sequential binary classification. In this way, the joint temporal and spatial information extraction proposal can not only detect shape anomalies with high precision and low false alarm rate but also retain the capability of generating explainable heatmaps to localize satellite shape anomaly components. Extensive experimental results confirm the superiority of the proposal.