基于等距映射和空间信息的光谱解混算法

由于多重反射和散射,高光谱图像中的混合像元实际上是非线性光谱混合。传统的光谱解混算法是以线性光谱混合模型为基础,因此解混精度不高。本文在光谱非线性混合模型的基础上,提出一种将等距映射与空间信息结合的非线性光谱解混算法。该算法通过等距映射算法将原始高光谱数据非线性降维到低维空间,并结合空间信息实现端元提取。得到的端元采用全约束的最小二乘法计算相应丰度。真实高光谱遥感数据实验结果表明,采用该算法得到的结果优于N-FINDR算法和基于测地线距离的最大单形体体积(GSVM)算法。

Hyperspectral demixing method based on isometric mapping and spatial information

Mixed pixel in hyperspectral image is actually a nonlinear spectral mixing,which is caused by multiple reflecting and scattering.Because traditional spectral demixing algorithms are based on linear spectral mixture model,they have low demixing precision.A nonlinear spectral demixing algorithm based on isometric mapping and spatial information is presented under nonlinear mixing assumptions.The algorithm reduces original data into a low-dimensional space by isometric mapping,and spatial information is used to extract endmembers from low-dimensional space.The abundances of the obtained endmembers are calculated by full-constrained least squares. Experimental results on real hyperspectral data demonstrate that the proposed approach outperformed N-FINDR method and the geodesic simplex volume maximization(GSVM).