Multi-Biased Models for Hyperspectral Anomaly Detection: A Paradigm to Improve Performance and Generalizability

Hyperspectral anomaly detection (HAD) poses a significant challenge as it requires modeling data with hundreds of measurements for each location in space. Many algorithms have been proposed to address problems in HAD, but most originate from one of several biases assumed of the data. This means that disparities between bias and variance can be observed among the algorithms in terms of their performance on individual datasets and more broadly across a diverse range of datasets. Ensemble learning enables the amalgamation of information across multiple biases to attenuate the trade-offs between bias and variance, improving individual dataset performance and generalizability across multiple datasets. Despite some work employing ensemble learning in HAD, amalgamating diverse HAD biases is an unexplored research direction. It is not clear whether amalgamating HAD biases improves performance, or what types and quantities of biases should be included and to what extent. To this end, this study employs 5 different ensembling methods to amalgamate 5 unique HAD biases to identify anomalies in 14 diverse datasets. The ensembling methods implemented consider equal, unequal, sparse, minimal, and mixed contributions among the biases. Results indicate that multi-biased models outperform single-biased models across all 14 datasets. In 12 of the 14 datasets peak performance was achieved by excluding or minimizing contribution to some of the biases, indicating that a mixture of sparse and minimal contributions was optimal. The results furnish empirical evidence as to the efficacy of multi-biased models to improve individual and generalized dataset performance, hence attenuating the bias-variance trade-off observed in the single-biased models. The results additionally provide direction for the most effective amalgamation strategies to construct optimal multi-biased HAD models.