A Novel Endmember Bundle Extraction and Clustering Approach for Capturing Spectral Variability Within Endmember Classes

Abstract

Spectral variability, unrelated to the purity of endmembers, can change the geometry of the dataspace and affect conventional methods used to identify endmembers. Several methods have been developed to identify and extract endmember bundles representing the spectral variability within each endmember class. These methods, however, operate on the geometry of the dataspace. In addition, they commonly use $k$ -means clustering that requires a priori the number of endmember classes present in a scene and may fail to group endmember spectra representing spectral variability within each class. This paper introduces a novel approach, spectral curve-based endmember extraction (SCEE), which allows for the extraction and clustering of multiple spectra representing spectral variability within endmember classes. The significant differences between SCEE and conventional methods are: i) SCEE is based on the shape of a spectral curve, not the geometry of the data simplex; and ii) SCEE extracts multiple endmember bundle candidates representing a particular class, without a priori knowledge of the number of endmember classes in a scene. Once multiple endmember bundle candidates are identified, they are automatically grouped by sequential pairwise clustering in order to determine the final number of endmember classes. The performance of SCEE is compared with that of other state-of-the-art endmember bundle extraction methods using simulated data and hyperspectral imagery of a mine pit and Cuprite. Results showed that multiple endmember bundles identified by SCEE gave better matches with spectral variability of reference spectra than those by other methods and were better able to encompass the range of variability within each class.