基于同质区分割的非线性端元提取算法

端元提取是高光谱遥感图像混合像元分解的关键步骤。传统端元提取算法忽略了高光谱图像中地物空间分布相关性与非线性结构，制约了端元提取算法的精度。针对高光谱图像的空间关系与非线性结构，提出一种基于同质区分割的非线性端元提取算法。使用超像素分割方法将图像分割为若干同质区，利用流形学习构造高光谱图像数据的非线性结构，最后在同质区内提取端元并利用聚类方法优选端元。模拟和真实图像数据实验表明，该算法能够保证高光谱数据的非线性结构，端元提取结果优于其他传统线性端元提取方法，在低信噪比的情况下，可以保持较好的端元提取结果。

Nonlinear Endmember Extraction Algorithm Based on Homogeneous Region Segmentation

Endmember extraction is the key step of the mixed pixel decomposition in hyperspectral remote sensing images. Traditional endmember extraction algorithms ignore the spatial correlation and nonlinear structure of hyperspectral images, which restricts the accuracy of endmember extraction. Aiming at the spatial relationship and nonlinear structure of hyperspectral images, a nonlinear endmember extraction algorithm based on homogeneous region segmentation is proposed. The hyperspectral image was divided into several homogeneous regions using superpixel segmentation method, and the manifold learning method was used to ensure nonlinear structure of hyperspectral images, extracting preferred endmembers within homogeneous regions. Simulation data and real hyperspectral image experiments show that the algorithm in this paper can guarantee the nonlinear structure of hyperspectral data, and the endmember extraction results are better than other traditional linear endmember extraction methods. In the case of low signal-to-noise ratio, it can maintain a good endmember extraction results.