Genetic Algorithm-Based Weighted Constraint Target Band Selection for Hyperspectral Target Detection

Hyperspectral imaging (HSI) data pose both opportunities and challenges for target detection due to the high spectral resolution and vast data volume. Traditional band selection methods for HSI often prioritize image quality or information content, neglecting target distinctiveness in specific detection tasks. To address this issue, this work proposes a novel band selection method, genetic algorithm-based weighted constraint target band selection (GA-WCTBS), which utilizes an improved genetic algorithm to optimize band subsets for small target detection. GA-WCTBS prioritizes target distinctiveness and background clutter fluctuations by a proposed spectral signal-to-clutter ratio (SCR) inspired by the constraint target method, even in bands with lower image quality. It employs a genetic algorithm to consider the combinatorial potential of bands for optimal detection. Additionally, a k-means and weight assignment strategy improves the background estimation for selecting a band subset with better clutter suppression capability. Experiments on widely used public ABU and AVIRIS datasets demonstrate that the band subset selected by GA-WCTBS significantly outperforms the existing methods in terms of detection capability.