基于分层的多端元光谱解混算法

高光谱图像中,单一端元光谱很难准确刻画一个类别,导致解混结果不准确。针对经典多端元光谱解混(MESMA)算法存在计算量大、端元预选繁琐等缺点,提出基于分层的MESMA(HMESMA)算法,第1层确定像元包含地物类别,第2层在第1层的基础上再分层确定像元包含最优端元个数。采用模拟数据和真实高光谱数据进行实验,证明了本文算法比固定端元解混效果好,平均丰度误差最高降低了2.65%,与经典的MESMA算法精度相当,但大大降低了计算量,提高了计算效率。

Multi-endmember hierarchical mixture analysis algorithm for spectra

In the traditional linear spectral mixture model,a feature class of t he hyperspectral image is represented by a single endmember.However,the spectral variability within a n endmember class is usually large because of wide space range and the feature complexity of the hyperspectral image. Under these conditions,a single endmember is difficult to portray a feature cate gory accurately, leading to incorrect unmixing results.Classical multi-endmember spectral unmixing algorithms play a positive role in overcoming the intra-class spectral variability,but there are shortcomings on large amount of calculation,cumbersome endmembers pre selection and so on.For these issues,we propose a hierarchical multi-endmember spec tral mixture analysis algorithm.The first layer is to determine the feature category by solv ing the maximum unmixing abundance error,and the second is stratified to find the optimal numbe r of endmemers contained in the pixels on the basis of the first layer.Simulated data and real hyperspectral data experiments prove that the proposed algorithm is better than the fixed end member unmixing algorithms,and the average abundance error cuts down by 2.65% at most,while comp ared with MESMA algorithm,the proposed algorithm reduces the comp utation and improves computational efficiency greatly,with almost the same preci sion.