GMoE-AD: Generalized Hyperspectral Anomaly Detection via Mixture-of-Experts and Domain-Invariant Learning.

Overview

1. Computer Science Department, The University of Memphis, Memphis, TN 38152, USA
2. Electrical and Computer Engineering Department, The University of Memphis, Memphis, TN 38152, USA; (A.R.); (C.P.)

Abstract

Hyperspectral (HS) sensing provides detailed high-dimensional spectral data to identify subtle anomalies and material variations in complex scenes. HS anomaly detection (HS-AD) aims to identify small, spectrally distinct objects or materials in HS imagery without prior target information. However, most HS-AD methods assume that the training and test data are drawn from the same distribution (single-domain). This assumption is often violated in operational sensing because of differences in sensor characteristics, scene composition, atmospheric conditions, and acquisition geometry. To address this challenge, we propose Generalized MoE-AD (GMoE-AD), a neural Mixture-of-Experts (MoE) architecture for robust cross-domain hyperspectral anomaly detection. The framework fuses the outputs of six unsupervised base detectors with representations from a pretrained HS foundation model, combines four neural experts through learned top-2 routing, and applies gradient-reversal-based domain-adversarial training to improve robustness under distribution shifts. A drift-aware test-time adaptation (DTA) variant is evaluated separately. We evaluate GMoE-AD on six public real-world HS benchmark datasets—San-Diego, Salinas, HYDICE-Urban, ABU-Airport, ABU-Beach, and ABU-Urban—and one private Arizona dataset comprising 22 images acquired by four different sensors. Under all-domain training, the model uses the training portions of all seven datasets and is evaluated without access to domain identity or dataset-specific information. GMoE-AD achieves an average ROC-AUC of 0.943, PR-AUC of 0.623, and F1-macro of 0.821 in this setting. In a leave-one-dataset-out (LODO) evaluation, where the target dataset is completely unseen during training, the model maintains an average ROC-AUC of 0.910 and F1-macro of 0.773. The optional DTA variant increases mean ROC-AUC from 0.938 to 0.953 but reduces F1-macro from 0.822 to 0.815, indicating a metric- and dataset-dependent adaptation trade-off. These results suggest that combining neural expert routing, transfer learning, and domain-adversarial representation learning improves robustness across heterogeneous HS datasets and provides a unified approach to anomaly detection in high-dimensional sensor data, supporting deployment in real-world hyperspectral applications where training and deployment conditions differ.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data Availability Statement

The six public hyperspectral datasets analyzed in this study are available from the repositories cited in Section 3.11. The Arizona dataset and the source code supporting the reported results are available from the corresponding author upon reasonable request, subject to applicable data-sharing restrictions.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 2 funders, 51 references.

Cite

This paper

Hossain, M., Robinson, A., Preza, C., & Wang, L. (2026). GMoE-AD: Generalized Hyperspectral Anomaly Detection via Mixture-of-Experts and Domain-Invariant Learning. Sensors (Basel, Switzerland), 26(17), 5661. https://doi.org/10.3390/s26175661

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