STIM: A Unified Spatially Informed Model for Robust Hyperspectral Anomaly Detection

Hyperspectral anomaly detection faces fundamental challenges in balancing spatial context, statistical rigor, and interpretability without ground truth supervision. This article presents spatially informed theoretical model (STIM), a novel unsupervised framework that addresses these challenges through a principled two-stage reference computation architecture. STIM systematically aggregates local spectral statistics into globally informed spatial references, enabling the derivation of three complementary features: energy (photometric deviation), entropy (local spectral coherence), and divergence (global statistical rarity). We establish theoretical foundations including noise robustness, Lipschitz continuity, and information-theoretic optimality with convergence guarantees. Comprehensive validation on five Airborne Visible/Infrared Imaging Spectrometer—Next Generation benchmark datasets demonstrates STIM's substantial superiority over traditional statistical and deep learning methods, achieving 14.6× to 585× improvements in mean anomaly scores with a reliability index of 0.933. Feature dynamics analysis confirms multimodal orthogonality and consistent interpretability across diverse hyperspectral environments. STIM enables robust, interpretable, and generalizable anomaly detection for operational hyperspectral imaging without requiring labeled supervision or scene-specific calibration, advancing the state-of-the-art in unsupervised hyperspectral analysis.