Physics-Informed Anomaly Detection of Terrain Material Change in Radar Imagery

TL;DR

This paper introduces a physics-informed approach for detecting terrain material changes in radar imagery using a lightweight electromagnetic model and unsupervised detectors, improving anomaly detection in complex clutter environments.

Contribution

It proposes a novel physics-aware feature extraction method and evaluates multiple unsupervised detectors on synthetic data, demonstrating improved detection performance.

Findings

Coherence and robust covariance enhance anomaly detection.

Score-level fusion yields the best F1 score in cluttered scenes.

Physics-informed features outperform traditional methods.

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.