CAJ | 학술논문

针对高光谱图像中端元的可变性和光谱的非线性混合特性，提出一种基于端元优化的非线性光谱解混算法，通过加入阴影端元对混合像元的端元集进行优化，对优化的端元子集采用基于分层贝叶斯模型的双线性光谱分解算法进行光谱分解。模拟数据和真实数据实验表明，提出的算法能很好地解决高光谱图像中存在的阴影效应，分解效果优于 FCLS 和 GBM算法。
침대고광보도상중단원적가변성화광보적비선성혼합특성，제출일충기우단원우화적비선성광보해혼산법，통과가입음영단원대혼합상원적단원집진행우화，대우화적단원자집채용기우분층패협사모형적쌍선성광보분해산법진행광보분해。모의수거화진실수거실험표명，제출적산법능흔호지해결고광보도상중존재적음영효응，분해효과우우 FCLS 화 GBM산법。
A nonlinear hyperspectral unmixing algorithm based on the optimal endmember subset is given to overcome the endmember variability and nonlinear mixed characteristics of hyperspectral data.Endmember sets of mixed pixels are optimized by adding a shadow endmember.The spectral of optimal endmember subset are decomposed by bilinear spectral decomposition algorithm based on hierarchical Bayesian model.Simulated experiments and real data show that OE-GBMalgorithm can solve shadow effect of hyperspectral images,and the accuracy of OE-GBM is better than that of FCLS and GBMalgorithm.