Hyperspectral Data Processing: Algorithm Design and Analysis

17.1 Introduction

With high spectral/spatial resolution many unknown material substances can be revealed by hyperspectral imaging sensors for data exploitation, specifically LSMA where a set of signatures used to form a linear mixing model may not be known by prior knowledge or be identified visually. Under such circumstances performing SLSMA with assumed target knowledge assumed a priori or obtained by visual inspection may not be realistic or applicable to real-world problems. Therefore, it is highly desirable to obtain the desired signature knowledge directly from the data without appealing for prior knowledge. In doing so, two major issues need to be addressed: (1) the number of signatures, denoted by p, used to form a linear mixing model and (2) a set of appropriate p signatures, , used to unmix data. Both issues are very challenging because determining the value of p and finding a desired set of p signatures must be conducted by an unsupervised means.

Since a hyperspectral signature is obtained by hundreds of contiguous spectral channels, the spectral correlation across all the spectral bands is very crucial and useful for material identification. In this chapter, we introduce a new concept of so-called spectral targets to differentiate spatial targets commonly addressed in ...