多光谱影像混合像元解混的加权变异系数分析法

影像上同物异谱或同谱异物的现象会造成同一混合像元端元在影像上的光谱不唯一。端元差异问题将给端元选择和提取造成困难,并最终影响混合像元分解的精度。为了尽可能减小端元类内差异、扩大类间差异,针对传统算法无法避免端元在不同波段的光谱数值尺度相差很大且定权自动化程度低的缺陷,将变异系数概念引入端元差异问题研究中,提出一种适用于多光谱数据的基于加权理论的加权变异系数分析法(Weighted coefficient of variation analysis,WCVA)。分别从理论和实验两方面论证了WCVA的可行性与优越性。在对比实验中,利用同一地区的TM和Geo Eye多光谱影像,从可视化端元空间分布、算法效率和混合像元最终解混精度比较了WCVA和最佳指数因子(Optimal index factor,OIF)结果。实验证明利用本文提出的WCVA方法获得的波段组合具有更高的解混精度(0.183和0.160)。同时运算效率明显高于OIF。因此WCVA不仅能够有效解决端元差异问题,提高混合像元解混的精度,而且具有较高的运算效率。

Novel Weighted Coefficient of Variation Analysis Approach for Endmember Variability Issue in Unmixing Process of Multi-spectral Imagery

The phenomena of different objects having the same spectrum and the same objects having different spectrum bring inconsistency for the same endmember. The existing of endmember variability issue will lead the process of endmember selection and extraction more difficult and decrease the final unmixing accuracy. Aiming to minimize the intra class variability and maximize the inter class variability, a new method named weighted coefficient of variation analysis (WCVA), which permitted the comparison of variants free from scale effects and made the weighting become more automatic, was proposed for multi spectral data. It was on the basis of coefficient of variation (CV) and weighting theory. The proposed method was successfully indicated from theoretical and experimental parts. The comparison with the commonly used optimal index factor (OIF) was conducted in terms of visualizing the spatial distribution of all available band combinations, efficiency and the final unmixing accuracy by fully constrained least squares (FCLS) and post polynomial post nonlinear mixture (PPNM) with TM and GeoEye images in the same research area. In the experimental results, the unmixing accuracy (0.183 and 0.160) based on the feature combination selected by WCVA was higher than that by OIF. Meanwhile, the computation of WCVA was much less than that of OIF as well. The results showed that WCVA not only had benefits for solving endmember variability issue and enhancing the unmixing accuracy, but also had higher efficiency.