红外与毫米波学报2011，Vol.30Issue(2)：131-136,155,7.
基于独立分量分析的高光谱遥感图像混合像元盲分解
Blind unmixing based on independent component analysis for hyperspectral imagery
摘要
Abstract
In hyperspectral unmixing, endmember signals are not independent with each other, which compromise the application of independent component analysis (ICA) algorithm. This paper presented a novel approach based on constrained ICA for hyperspectral unmixing to overcome this problem. By introducing the constraints of abundance nonnegative and abundance sum-to-one, the purpose of our algorithm was not to find independent components as decomposition results anymore. In order to accord with the condition of hyperspectral imagery, we developed an abundance modeling technique to describe the statistical distribution of the data. The modeling approach is capable of serf-adaptation, and can be applied to hyperspectral images with different characteristics. Experimental results on both simulated and real hyperspectral data demonstrated that the proposed approach can obtain more accurate results than the other state-of-the-art approaches. As an algorithm with no need of spectral prior knowledge, our method provided an effective technique for the blind unmixing of hyperspectral imagery.关键词
高光谱解混/独立分量分析/丰度非负约束/丰度和为一约束Key words
hyperspectral unmixing/ independent component analysis (ICA)/ abundance nonnegative constraint(ANC)/ abundance sum-to-one constraint(ASC)分类
信息技术与安全科学引用本文复制引用
夏威,王斌,张立明..基于独立分量分析的高光谱遥感图像混合像元盲分解[J].红外与毫米波学报,2011,30(2):131-136,155,7.基金项目
863国家高技术研究计划(2009AA12Z115) （2009AA12Z115）
国家自然科学基金(61071134,60672116) （61071134,60672116）
