Abstract
Preprocessing is a major area of interest in the field of hyperspectral endmember extraction, for it can provide a few high-quality candidates for fast endmember extraction without sacrificing endmember accuracy. We propose a superpixel-guided preprocessing (SGPP) algorithm to accelerate endmember extraction based on spatial compactness and spectral purity analysis. The proposed SGPP first transforms a hyperspectral image into low-dimension data using principal component analysis. SGPP then utilizes the superpixel method, which normally has linear complexity, to segment the first three components into a set of superpixels. Next, SGPP transforms low-dimension superpixels into noise-reduced superpixels and calculates their spatial compactness and spectral purity based on Tukey’s test and data convexity. SGPP finally retains a few high-quality pixels from each superpixel with high spatial compactness and spectral purity indices for subsequent endmember identification. Based on the spectral angle distance, root-mean-square error, and speedup, experiments are conducted on synthetic and real hyperspectral datasets, and they indicate that SGPP is superior to current state-of-the-art preprocessing techniques.
Highlights
Due to the limited spatial resolution of a hyperspectral sensor, different materials can jointly occupy a single pixel, which occurs as a mixed pixel in a hyperspectral image
The last three decades have witnessed a huge growth in endmember extraction algorithms (EEAs) because the exploitation of endmembers is a prerequisite to accurate estimation of abundance fractions
To improve the noise robustness and reduce the computational burden, this paper outlines a superpixel-guided preprocessing (SGPP) algorithm based on spatial compactness and spectral purity
Summary
Due to the limited spatial resolution of a hyperspectral sensor, different materials can jointly occupy a single pixel, which occurs as a mixed pixel in a hyperspectral image. Such spectral-based EEAs involve finding endmembers from entire pixels, which is time-consuming In this regard, numerous spatial–spectral-based preprocessing algorithms (PPAs) have been proposed; these are independent modules that generally utilize both spatial and spectral information with the intent to offer a few high-quality candidates for fast endmember extraction without endmember accuracy loss. To improve the noise robustness and reduce the computational burden, this paper outlines a superpixel-guided preprocessing (SGPP) algorithm based on spatial compactness and spectral purity. Compared with the current PPAs, SGPP still has a similar preprocessing strategy that removes redundant pixels from the image by jointing spatial–spectral information, it achieves lighter computation time and higher noise robustness abilities.
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