Hyperspectral Data Processing: Algorithm Design and Analysis

21.1 Introduction

Hyperspectral images are collected in hundreds of contiguous spectral channels. Therefore, not only the data volume to be processed is considered to be huge, but also the spectral correlation among bands is expected to be very high due to high spectral resolution. Band selection (BS) is one of commonly used approaches that take advantage of such high interband correlation to remove band redundancy. Over the past years, many research efforts have been directed to BS (Mausel et al., 1990; Conese and Maselli, 1993; Stearns et al., 1993; Chang et al., 1999; Huang and He, 2005; Chang and Wang, 2006; Du et al., 2007) in order to achieve a wide range of applications such as data compression, data storage, data transmission, and communication. Generally, two crucial issues arising in BS need to be resolved, which are (1) number of bands required for BS and (2) what criterion to be used to select bands. Instead of dealing with BS directly this chapter introduces a new concept of band prioritization (BP) that is similar to the DP developed in Chapter 20 to simultaneously address these two issues by prioritizing all spectral bands in some optimal sense. It ranks all the spectral bands in accordance with their corresponding priority scores that can be calculated by a custom-designed BP criterion in a similar way that spectral dimensions are prioritized by DP in Chapter 20. Then PBDP can be performed by BP in such a manner that two dual processes, progressive band dimensionality ...