Enhancing Hyperspectral Anomaly Detection Algorithm Comparisons: Leveraging Dataset and Algorithm Characteristics

Validating the contributions of new algorithms is a critical step in hyperspectral anomaly detection (HAD) research. Typically, validation involves comparing the performance of a proposed algorithm against other algorithms using a series of benchmark datasets. Despite the longstanding use of this comparison process, little attention has been paid to the characteristics of datasets and algorithms that ensure each algorithm has an equal opportunity of performing well. Characteristics of datasets and algorithms that inadvertently favor one algorithm can skew results, leading to misleading conclusions. To address this issue, this study introduces a feature-centric framework designed to assist in ensuring an unbiased comparison of HAD algorithms. The framework identifies significant correlations between datasets and algorithms by extracting distribution-related features from the datasets and statistically testing them against the algorithmic outcomes. The identified trends are then compared across datasets to ensure that all relevant trends are equally represented, thereby ensuring diversity and validating that no singular algorithm is afforded an inherent advantage. The framework was tested on five algorithms across 14 datasets. The results indicate that multiple measures of variance within the datasets are key drivers of diversity, and these measures accurately predicted algorithmic outcomes for 12 of the 14 datasets. This suggests that the identified trends effectively explain the algorithmic outcomes and highlights the importance of incorporating datasets with a diverse range of variances in comparisons of HAD algorithms.