Abstract
ABSTRACTHyperspectral sensors often collect hundreds of bands at a time, so hyperspectral images can accurately characterize different land-cover types with abundant spectral information. However, these spectral bands also contain redundant information that needs to be removed. Band selection is one of the most widely used methods to remove noised or redundant bands. Because labelled samples are difficult to collect, most band selection methods adopt unsupervised ways to select diverse and representative bands. Still, noised bands are often selected because they usually have low correlation with other bands. In this article, objective image quality assessment is introduced to indicate the quality of every band, and combined with the redundancy measure, a new unsupervised band selection method is proposed. Three real hyperspectral images are used to demonstrate the effectiveness of the proposed algorithm.
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