基于波段选择改进的高光谱端元提取方法

为了解决传统N-FINDR算法降维时破坏像元光谱曲线的物理意义这个问题,采用波段选择方法中的最佳指数法代替特征提取,改进N-FINDR算法的降维方式;利用模拟和真实高光谱数据进行实验,分别用改进的N-FINDR算法与其它两种算法提取端元,并用全约束最小二乘法解混.结果表明,改进的N-FINDR算法的解混精度更高,用时更少.用波段选择代替特征提取改进降维方式,保留了光谱曲线的物理意义,在N-FINDR算法中是可行的.

An improved method of hyperspectral endmember extraction based on band selection

In order to solve the problem of destroying the physical meaning of spectral curve of pixels in dimension reduction of traditional N-FINDR algorithm, the best exponential method of band selection was used instead of feature extraction. The dimension reduction method of N-FINDR algorithm was improved. Experiments were carried out using the simulated and real hyperspectral data. The improved N-FINDR algorithm and other two algorithms were used to extract the terminal elements respectively. Full constrained least squares method was used to solve the mixing problem. The results show that the improved N-FINDR algorithm has higher precision and uses less time. It is feasible to use band selection instead of feature extraction to improve the dimension reduction method and retain the physical meaning of spectral curve in N-FINDR algorithm.