由于不同的照明条件、复杂的大气环境等因素,相同端元的光谱特征在图像的不同位置呈现出可见的差异,这种现象被称为端元的光谱变异性.在相当大的场景中,端元的变异性可能很大,但在适度的局部同质区内,变异性往往很小.扰动线性混合模型(Perturbed Linear Mixing Model,PLMM)在解混的过程中可以减轻端元变异性造成的不利影响,但是对缩放效应造成的变异性的处理能力较弱.为此,本文改进了扰动线性混合模型,引入了尺度因子以处理缩放效应造成的变异性,并结合超像素分割算法划分局部同质区,然后设计出基于局部同质区共享端元变异性的解混算法(Shared Endmember Variability in Unmixing,SEVU).与扰动线性混合模型,扩展线性混合模型(Extended Linear Mixing Mod-el,ELMM)等算法相比,所提SEVU算法在合成数据集上平均端元光谱角距离(mean Spectral Angle Distance,mSAD)和丰度均方根误差(abundance Root Mean Square Error,aRMSE)最优,分别为0.085 5和0.056 2;在Jasper Ridge和Cu-prite真实数据集上mSAD是最优的,分别为0.060 3和0.100 3.在合成数据集和两个实测数据集上的实验结果验证了SEVU算法的有效性.
Hyperspectral unmixing with shared endmember variability in homogeneous region
Due to different lighting conditions,complex atmospheric conditions and other factors,the spectral signatures of the same endmembers show visible differences at different locations in the image,a phenomenon known as spectral variability of endmembers.In fairly large scenarios,the variability can be large,but within moderately localised homogeneous regions,the variability tends to be small.The per-turbed linear mixing model(PLMM)can mitigate the adverse effects caused by endmember variability dur-ing the unmixing process,but is less capable of handling the variability caused by scaling utility.For this reason,this paper improved the perturbed linear mixing model by introducing scaling factors to deal with the variability caused by the scaling utility,and used a super-pixel segmentation algorithm to delineate lo-cally homogeneous regions,and then designed an algorithm of Shared Endmember Variability in Unmix-ing(SEVU).Compared with algorithms such as perturbed linear mixing model,extended linear mixing model(ELMM),and other algorithms.The proposed SEVU algorithm was optimal in terms of mean Endmember Spectral Angular Distance(mSAD)and abundance Root Mean Square Error(aRMSE)on the synthetic dataset with 0.085 5 and 0.056 2,respectively.mSAD is optimal on the Jasper Ridge and Cuprite real datasets with 0.060 3 and 0.100 3,respectively.Experimental results on a synthetic dataset and two real datasets verify the effectiveness of the SEVU algorithm.
hyperspectral imageunmixingspectral variabilityperturbed linear mixing modellocal ho-mogeneous region
万方数据
维普
