A Band Selection Method for Sub-Pixel Target Detection in Hyperspectral Images Based on Laboratory and Field Reflectance Spectral Comparison

Abstract

ABSTRACT:In recent years, developing target detection algorithms has received growing interest in hyperspectral images. In comparison to the classification field, few studies have been done on dimension reduction or band selection for target detection in hyperspectral images. This study presents a simple method to remove bad bands from the images in a supervised manner for sub-pixel target detection. The proposed method is based on comparing field and laboratory spectra of the target of interest for detecting bad bands. For evaluation, the target detection blind test dataset is used in this study. Experimental results show that the proposed method can improve efficiency of the two well-known target detection methods, ACE and CEM.

Selecting optimal bands for sub‐pixel target detection in hyperspectral images based on implanting synthetic targets

“…However, a substantial number of bands lead to heavy computational costs [2, 3] along with Hughes effect [4] on hyperspectral image processing. Hence, in recent years, much attention has been paid to the reduction of computational complexity in the processing of hyperspectral images, particularly in the field of target detection [5–16]. Feature selection (of the band) and extraction are two main approaches of data dimension reduction [17].…”