结合分层和ADMM的高光谱图像解混方法

Hyperspectral image unmixing method combining hierarchy and alternating direction method of multipliers(ADMM)

高光谱图像有较高的光谱分辨率,但是单个像元覆盖的面积比较大,导致单个像元中出现多于一种地物的现象,即混合像元。混合像元的存在严重影响了高光谱数据的后续利用。高光谱图像解混技术的目的就是将混合像元中存在的地物种类(端元)以及各个地物种类所对应的比例(丰度)精确地表示出来。高光谱数据覆盖的范围比较大,不可避免存在端元变异的现象。为了应对端元变异现象,利用扩展的线性混合模型对高光谱数据进行建模。在基于分层解混技术的基础上,利用乘子交替方向法对其进行优化。实验结果表明,解混效果得到提升。

Hyperspectral image has a higher spectral resolution,but the area covered by a single pixel is relatively large,resulting in more than one material exist in a single pixel,called a mixed pixel.The presence of mixed pixels severely affects the subsequent use of hyperspectral data.The purpose of hyperspectral image unmixing technique is to determine the materials(endmembers)present in the mixed pixels and their corresponding proportions(abundance).Because the coverage of hyperspactral data is relatively large,the phenomenon of endmember variation exists inevitably.In order to take endmember variation into consideration,an extended linear mixed model is used to describe the hyperspectral data.Based on the hierarchical unmixing technique,the alternating direction method of multipliers is used to optimize the result of unmixing.The experimental results show that the unmixing effect has been greatly improved.