基于测地线距离和正交投影的端元提取算法

Endmember Extraction Algorithm Based on Geodesic Distance and Orthogonal Projection

摘要:

端元提取是高光谱遥感图像混合像元分解的关键步骤。传统线性端元提取方法忽略了像元内地物的非线性混合因素,制约了混合像元分解精度的提升。针对高光谱图像数据的非线性结构,提出一种基于测地线距离的正交投影端元提取算法,将测地线距离引入端元单体提取过程,利用正交投影方法逐个提取端元。为了降低测地线距离计算量,在端元提取前先利用自动目标生成方法和无约束最小二乘法对原始高光谱数据进行数据约减。模拟和真实高光谱图像实验表明,该方法能够表征光谱数据中非线性因素,端元提取结果优于传统自动目标生成端元提取方法。

Abstract:

Endmember extraction is a key step in the decomposition of mixed pixels in hyperspectral remote sensing images. The traditional linear endmember extraction method ignores the nonlinear mixing factors of the geological features in the pixel, which restricts the improvement of the decomposition accuracy of the mixed pixel. Considering the nonlinear structure of hyperspectral image data, an orthogonal projection endmember extraction algorithm based on geodesic distance (OPEEGD) is proposed. The geodesic distance was introduced into the endmember extraction process, and the endmembers were extracted one by one using the orthogonal projection method. In order to reduce the amount of geodesic distance calculation, the original hyperspectral data was reduced by the automatic target generation process method and the unconstrained least squares method before endmember extraction. Experiments on simulated and real hyperspectral remote sensing images show that this method can characterize nonlinear factors in spectral data, and the endmember extraction results are better than traditional automatic target generation process endmember extraction method.