Hyperspectral Data Processing: Algorithm Design and Analysis

7.1 Introduction

What makes endmember extraction unique in hyperspectral data exploitation is that an endmember represents the purity of a spectral signature that can be used to specify a spectral class. Interestingly, endmember extraction has not received much attention in multispectral data analysis in the last decades because low spectral and spatial resolutions of multispectral imaging sensors result in collected data sample vectors, which are more likely to be mixed instead of being pure. Consequently, the likelihood of finding endmembers is rather small. Under such circumstances, there is no reason to perform endmember extraction in multispectral data other than conducting mixed data analysis. However, with the use of high spatial and spectral resolution bands many subtle material substances that cannot be resolved by multispectral sensors can be now revealed by hyperspectral imaging sensors as pure signatures. Such substances generally provide vital information in image analysis. One of such substances is endmembers that may appear as mixed sample vectors in multispectral data but turn out to be pure signature vectors in hyperspectral data. Accordingly, finding endmembers has emerged as a fundamental and critical data preprocessing that offers basic understanding of hyperspectral data. On the other hand, finding these substances is a big challenge since their existence generally cannot be known by prior knowledge or visualized by inspection from their spatial presence.