Nie Mingyu, Liu Zhi, He Xiaofu, Qiu Qingchen, Zhang Yuanyuan, Chang Ju
Appl Opt. 2017 Mar 20;56(9):2476-2482. doi: 10.1364/AO.56.002476.
Hyperspectral images collected by a remote sensing hyperspectral imaging instrument have many mixed pixels, due to the limited resolution of image sensors and the complex diversity of nature. End-member extraction is the process that determines the end-members in mixed pixels. The results of traditional methods are inaccurate, due to the spatial complexity and noise of actual hyperspectral image data. This study presents segmented vertex component analysis (SVCA), wherein the relative complexities of hyperspectral images are segmented into a number of relatively simple spatial subsets to reduce the effect of uncorrelated pixels. The end-members are extracted by finding the vertices of the simplex that minimally encloses the hyperspectral image data in each spatial subset, and the inversion abundance is used to identify each major end-member in each subset. Experimental results demonstrate that the proposed method can effectively implement end-member extraction with high accuracy.
由于图像传感器分辨率有限以及自然环境的复杂多样性，通过遥感高光谱成像仪器采集的高光谱图像存在许多混合像素。端元提取是确定混合像素中端元的过程。由于实际高光谱图像数据的空间复杂性和噪声，传统方法的结果不准确。本研究提出了分段顶点成分分析（SVCA），其中将高光谱图像的相对复杂性分割成多个相对简单的空间子集，以减少不相关像素的影响。通过找到在每个空间子集中最小包围高光谱图像数据的单纯形的顶点来提取端元，并使用反演丰度来识别每个子集中的每个主要端元。实验结果表明，该方法能够有效地高精度地实现端元提取。
