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.

中文翻译：

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基于空间光谱分析的超像素引导预处理算法加速高光谱端元提取

预处理是高光谱端元提取领域的一个主要兴趣领域，因为它可以在不牺牲端元精度的情况下为快速端元提取提供一些高质量的候选。我们提出了一种基于空间紧凑性和光谱纯度分析的超像素引导预处理（SGPP）算法来加速端元提取。所提出的 SGPP 首先使用主成分分析将高光谱图像转换为低维数据。SGPP 然后利用通常具有线性复杂度的超像素方法将前三个分量分割成一组超像素。接下来，SGPP 将低维超像素转换为降噪超像素，并根据 Tukey 测试和数据凸性计算它们的空间紧凑度和光谱纯度。SGPP 最终从每个超像素中保留了一些具有高空间紧凑性和光谱纯度指数的高质量像素，用于后续端元识别。基于光谱角距离、均方根误差和加速比，在合成和真实的高光谱数据集上进行了实验，结果表明 SGPP 优于当前最先进的预处理技术。