Endmember Bundle Extraction Based on Pure Pixel Index and Superpixel Segmentation

Abstract

Spectral unmixing is a fundamental issue that needs to be addressed in the application of hyperspectral images. Due to the complex imaging conditions in remote sensing, it is common for the same object to have different spectral signatures. In this paper, we present a novel endmember bundle extraction method based on pixel purity index and superpixel segmentation to deal with this problem, where each material is represented by a set of similar endmem-ber spectra. This method improves the accuracy of endmember extraction and focuses on the removal of redundant endmembers, leading to less spectral unmixing error than existing endmember bundle extraction algorithms. The experimental results on both synthetic dataset and real dataset demonstrate that the proposed method performs effectively in extracting variable endmember sets.