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首页> 外文会议>International Geoscience and Remote Sensing Symposium >Sparse graph embedding dimension reduction for hyperspectral image with a new spectral similarity metric
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Sparse graph embedding dimension reduction for hyperspectral image with a new spectral similarity metric

机译：使用新的光谱相似度度量的高光谱图像的稀疏图嵌入维数减少

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摘要

Graph embedding, as a dimensionality reduction framework, has already drawn great attention in hyperspectral image analysis. Taking locality preserving projection (LPP) as example, LPP utilizes typical Euclidean distance in heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral variation of a material, which may result in inappropriate graph representation. In this work, a graph-based discriminant analysis with novel spectral similarity measurement is proposed, which fully considers curves changing description among spectral bands. Experimental results based on real hyperspectral images demonstrate the proposed method is superior to traditional methods, such as supervised LPP, and the state-of-the-art sparse graph-based discriminant analysis (SGDA).
机译：图形嵌入作为降维框架，已经在高光谱图像分析中引起了极大的关注。以局部保留投影（LPP）为例，LPP利用热核中的典型欧几里得距离来创建亲和矩阵，并将高维数据投影到低维空间中。但是，欧氏距离与材料的固有光谱变化没有足够的相关性，这可能导致不适当的图形表示。在这项工作中，提出了一种基于图的判别分析和新颖的光谱相似性测量，该分析充分考虑了曲线在光谱带之间的变化描述。基于真实高光谱图像的实验结果表明，该方法优于传统方法，例如监督LPP和基于稀疏图的最新判别分析（SGDA）。

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