CAJ | 학술논문

针对经典多端元光谱混合模型( MESMA)存在着计算量大,端元预选繁琐以及过拟合等缺点,提出了一种改进的多端元解混算法. 该算法根据正交子空间投影具有分离感兴趣信号与不感兴趣信号的特点,将像元投影到全部地物端元(每类地物选择一条类内光谱)构成的正交子空间上,按照投影值确定构成混合像元每类地物的类内光谱,在下一步迭代求解的过程中,分离出已确定地物类内光谱的像元,降低计算量,然后根据重构误差变化量确定最优端元个数,避免过拟合. 实验结果表明,改进的算法反演丰度误差和解混时间都比原有算法降低很多.
침대경전다단원광보혼합모형( MESMA)존재착계산량대,단원예선번쇄이급과의합등결점,제출료일충개진적다단원해혼산법. 해산법근거정교자공간투영구유분리감흥취신호여불감흥취신호적특점,장상원투영도전부지물단원(매류지물선택일조류내광보)구성적정교자공간상,안조투영치학정구성혼합상원매류지물적류내광보,재하일보질대구해적과정중,분리출이학정지물류내광보적상원,강저계산량,연후근거중구오차변화량학정최우단원개수,피면과의합. 실험결과표명,개진적산법반연봉도오차화해혼시간도비원유산법강저흔다.
The classical multi-endmember spectral mixture analysis model has shortcomings in computation intensi-ty, cumbersome endmember preselection and over-fitting. To overcome these shortcomings, an improved multi-end-member unmixing algorithm is proposed here. Using the characteristics of orthogonal subspace projection that can distinguish signals of interest, it projects pixels onto the orthogonal subspace composed of all of endmembers of the entire surface feature class. Each class selects only one intra-class spectrum. Then it determines the intra-class spectrum of every feature class to which pixels belong according to their projection values. These pixels are isolated in the next iteration in order to reduce computation. Then the optimal number of endmember combinations can be determined according to the reconstruction error variation, which avoids over-fitting. Experiment results show that the inversion abundance error and unmixing time of the improved algorithm are reduced compared to the original al-gorithm.