Non-Negative Latent Factorization Autoencoder for Interpretable Hyperspectral Anomaly Detection
Year: 2026
Type: article
Abstract: Hyperspectral anomaly detection (HAD) requires effective background modeling while limiting the reconstruction of rare anomalous spectra. Conventional non-negative matrix factorization provides an interpretable endmember–abundance representation but offers limited flexibility for estimating background representations under complex spectral variability, whereas reconstruction-oriented autoencoders provide flexible nonlinear inference but often lack an explicit mixing-model structure. To bridge these two paradigms, we propose a Non-negative Latent Factorization Autoencoder (NLFA) for anomaly-oriented background modeling. NLFA employs a nonlinear encoder to estimate simplex-constrained abundance-like latent coefficients and a linear mixing model (LMM)-consistent linear decoder to represent the dominant background through endmember-like spectral bases. A column-sparse residual is jointly optimized with the latent factorization so that spectral components insufficiently explained by the learned background are retained for anomaly detection, while local spatial smoothness and endmember-diversity constraints regularize the background representation. Experiments on eight benchmark hyperspectral datasets show that NLFA achieves competitive detection performance.
Source: Remote Sensing
DOI: 10.3390/rs18213781
Institution: Northwestern Polytechnical University
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W7225183758