Impact of informative band selection on target detection performance

Abstract

In this paper, the effect of dimensionality reduction of hyperspectral data on 10 subpixel target detectors is investigated. The genetic algorithm (GA) and wavelet feature extraction methods are used for dimensionality reduction as they maintain physically meaningful bands and physical structure of the spectra, respectively. In the former case, the wrapper method is used to improve subpixel target detectors' results in terms of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Meanwhile, in the latter case, the AUC is used as a criterion to choose the optimum level of wavelet decomposition. Experimental results obtained from a real-world hyperspectral data and a challenging synthetic dataset approved that band selection with the wrapper method is more efficient than using target detection methods without dimensionality reduction, especially in the presence of difficult targets at subpixel level.