Physics-Informed Anomaly Detection of Terrain Material Change in Radar Imagery
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915801880068096 |
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| author | Ahmed, Abdel Hakiem Mohamed Abbas Mohamed Jelfs, Beth Chapman, Airlie Schoof, Eric Gilliam, Christopher |
| author_facet | Ahmed, Abdel Hakiem Mohamed Abbas Mohamed Jelfs, Beth Chapman, Airlie Schoof, Eric Gilliam, Christopher |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15618 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Anomaly Detection of Terrain Material Change in Radar Imagery Ahmed, Abdel Hakiem Mohamed Abbas Mohamed Jelfs, Beth Chapman, Airlie Schoof, Eric Gilliam, Christopher Signal Processing 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. |
| title | Physics-Informed Anomaly Detection of Terrain Material Change in Radar Imagery |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.15618 |