Physics-Informed Anomaly Detection of Terrain Material Change in Radar Imagery
Abstract
In this paper we consider physics-informed detection of terrain material change in radar imagery (e.g., shifts in permittivity, roughness or moisture). We propose a lightweight electromagnetic (EM) forward model to simulate bi-temporal single-look complex (SLC) images from labelled material maps. On these data, we derive physics-aware feature stacks that include interferometric coherence, and evaluate unsupervised detectors: Reed-Xiaoli (RX)/Local-RX with robust scatter (Tyler's M-estimator), Coherent Change Detection (CCD), and a compact convolutional auto-encoder. Monte Carlo experiments sweep dielectric/roughness/moisture changes, number of looks and clutter regimes (gamma vs K-family) at fixed probability of false alarm. Results on synthetic but physically grounded scenes show that coherence and robust covariance markedly improve anomaly detection of material changes; a simple score-level fusion achieves the best F1 in heavy-tailed clutter.
Cite
@article{arxiv.2602.15618,
title = {Physics-Informed Anomaly Detection of Terrain Material Change in Radar Imagery},
author = {Abdel Hakiem Mohamed Abbas Mohamed Ahmed and Beth Jelfs and Airlie Chapman and Eric Schoof and Christopher Gilliam},
journal= {arXiv preprint arXiv:2602.15618},
year = {2026}
}