An Image-Based Endmember Bundle Extraction Algorithm Using Both Spatial and Spectral Information

With the development of imaging technology, remote sensing images with a high spatial and spectral resolution have become available and have been used in various applications such as the identification of materials and the estimation of physical parameters. Although many endmember extraction algorithms have been proposed for hyperspectral data sets which extract/select the standard endmember spectrum for each existing endmember class or scene component, there are still some problems in endmember extraction which blur the discrimination between the different types of ground objects and lead to inaccurate endmember extraction. One problem is that the definition of pure materials (or endmembers) can be subjective and application dependent. The other problem is that spectral variability is inevitable due to the different imaging conditions, especially in a hyperspectral image with a higher spatial resolution. In this paper, to account for the spectral variability, each endmember of a material is represented with a set or “bundle” of spectra, and an image-based endmember bundle extraction algorithm using both spatial and spectral information is proposed. There are four steps in the proposed method of extracting endmember bundles: 1) pixel purity index preprocessing; 2) homogeneity index calculation; 3) region-based candidate endmember selection; and 4) spectral clustering. Experiments with both synthetic and real hyperspectral data sets indicate that, by considering the endmember variability in the original hyperspectral data, the proposed method shows a significant improvement over the current state-of-the-art endmember bundle extraction methods.