A New Band Selection Method for Hyperspectral Image Based on Data Quality

Most unsupervised band selection methods take the information of bands into account, but few of them pay attention to the quality of bands. In this paper, by combining idea of noise-adjusted principal components (NAPCs) with a state-of-art band selection method [maximum determinant of covariance matrix (MDCM)], we define a new index Q to quantitatively measure the quality of the hyperspectral data cube. Both signal-to-noise ratios (SNRs) and correlation of bands are simultaneously considered in Q. Based on the new index defined in this article, we propose an unsupervised band selection method called minimum noise band selection (MNBS). Taking the quality (Q) of the data cube as selection criterion, MNBS tries to find the bands with both high SNRs and low correlation (high Q). The subset selection method, sequential backward selection (SBS), is used in MNBS to improve the search efficiency. Some comparative experiments based on simulated as well as real hyperspectral data are conducted to evaluate the performance of MNBS in this study. The experimental results show that the bands selected by MNBS are always more effective than those selected by other methods in terms of classification.