Framelet变换高光谱图像光谱加权稀疏解混

Spectral weighted sparse unmixing of hyperspectral images based on framelet transform

空域中高光谱数据由于信息过于分散,冗余过多,且易受噪声的影响,其特征提取难度较大。为了提高高光谱图像解混的鲁棒性和稀疏性,提出了一种framelet变换高光谱图像光谱加权稀疏解混方法。介绍了高光谱稀疏解混和framelet变换方法的理论知识,接着利用framelet变换对高光谱图像解混建模,并且在该模型上加入变换域光谱加权稀疏正则项,提出framelet变换的高光谱图像光谱加权稀疏解混模型。最后,利用交替方向乘子法对模型进行求解。实验结果表明:信号与重建误差比(SRE)提高12.4%~1045%,丰度重构正确率(Ps)保持在16%的误差内。与其他相关稀疏解混方法相比,本文提出的算法具有良好的抗噪性和稀疏性能,获得了更好的解混结果。

Hyperspectral sparse unmixing methods have attracted considerable attention,and most current sparse unmixing methods are implemented in the spatial domain;however,the hyperspectral data used by these methods complicate feature extraction owing to scattered information,redundancy,and noisy spatial signals.To improve the robustness and sparsity of the unmixing results of hyperspectral images,a spectral-weighted sparse unmixing method of hyperspectral images based on the framelet transform(SFSU)is proposed.First,we introduce the theoretical knowledge of hyperspectral sparse unmixing and the framelet transform.Following this,we develop a hyperspectral image unmixing model based on the framelet transform using this theory.In this model,a spectral-weighted sparse regularization term is added to construct the SFSU.Finally,to solve the SFSU model,an alternating direction method of multipliers is presented.According to the experimental results,the signal-to-reconstruction error ratio is found to increase by 12.4%-1045%,and the probability of success(Ps)remains within 16%error.The proposed model demonstrates better anti-noise and sparse performance compared with other related sparse unmixing methods and yields better unmixing results.