基于交互最小二乘优化的高光谱影像端元光谱分析

Endmember Analysis for Hyperspectral Imagery Based on Alternative Least Square Optimization

提出了基于交互最小二乘优化的高光谱影像端元光谱计算方法,利用ALS计算的灵活性将多种对组分丰度和被估计光谱的约束条件加入到ALS迭代计算中,以传统算法得到的端元光谱作为初始,并考虑数据的特殊性建立了适合于高光谱影像的端元分析方法。模拟数据分析和Cuprite矿区的光谱分析结果证明了本文算法能很好地处理不严格假设纯光谱存在情况下的端元提取问题。

Endmember extraction is very important in mixed spectral analysis,which aims to identify the pure source signal from the mixture.In the past decade,many algorithms have been proposed to perform this estimation.One commonly used assumption is that all the endmembers have pure pixel representation in the scene.When such pixels are absent,these algorithms can only return certain pixels that are close to the real endmembers.To overcome this problem,we present a pure spectral calculation method without the pure pixel assumption for hyperspectral image analysis.The method is based on the alternative least square optimization,for its flexibility in containing several constraints for abundance and spectral,which refers to nonnegative,equality,closure,normalization and simplex volume constraints.There are other three problems are exploited： First,the traditional endmember algorithms are used for initialization;Second,spatial redundancy reduction is included in the preprocess procedure.The experimental results based on synthetic toy example and Cuprite mine area hyperspectral scene demonstrate that the proposed method can handle the pure pixels absent problem very well.