高光谱分类体积的端元提取

Classification and Volume for Hyperspectral Endmember Extraction

为求解高光谱图像中各物质的分布及含量,将高光谱分类引入端元提取,提出了一种新的端元提取方法。首先利用虚拟维度评估端元数目;然后引入高光谱分类的思想,通过K-means聚类算法对高光谱图像进行非监督分类,对各类物质进行大致分类;在每类物质中提取出光谱值最大的像元,用这些像元构成端元候选集;最后,依据单形体理论,将高光谱图像的像元点在高维空间中构成单形体,体积最大的单形体的顶点即为端元。模拟和真实高光谱数据证明,此端元提取方法相对于传统方法具有高效、准确的优点。

In order to solve the distribution and content of each substance in hyperspectral images, we introduce the hyperspectral classification into the endmember extraction to propose a new endmember extraction method. Firstly, the number of endmembers is determined by the virtual dimension. And the thought of hyperspectral unsupervised classification is performed by K-means clustering algorithm which classifies each pixel into classifications. Then the pixeI with the largest spectral value is extracted from each kind of class. According to the theory of simplex, the pixels of hyperspectral image are used to form a simplex in the high-dimensional space, and the vertexes of the largest simplex are the endmembers which are extracted. The simulation and real data have shown that this method of endmember extraction has the advantages of high efficiency and accuracy compared with the traditional method.