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Selecting Band Subsets from Hyperspectral Image Through a Novel Evolutionary-Based Strategy

Hyperspectral dimensionality reduction by optimal band selection attracts wide attention recently because a few pivotal and physically meaningful bands can not only represent the whole image cube without losing effectiveness but also mitigate the computational burden. In this paper, we construct an efficient searching strategy based on the clonal selection principle to optimize a geometry-based criterion named maximum ellipsoid volume (MEV). The main contributions are two-fold: 1) a subtle relationship that can accelerate the calculation of the criterion and 2) an evolutionary strategy to relieve the heavy computational burden of obtaining the desired bands from numerous quality candidates. The experimental result on a real hyperspectral data demonstrates that the proposed method is effective.