基于空谱约束的加权稀疏柯西非负矩阵分解高光谱解混  

Weighted Sparse Cauchy Nonnegative Matrix Factorization Hyperspectral Unmixing Based on Spatial-Spectral Constraints

作　　者：陈善学[1,2] 胡之源 Chen Shanxue;Hu Zhiyuan(Chongqing University of Posts and Telecommunications,School of Communication and Information Engineering,Chongqing,400065,China;Engineering Research Center of Mobile Communications of the Ministry of Education,Chongqing,400065,China;Chongqing Key Laboratory of Mobile Communications Technology,Chongqing,400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065 [2]移动通信教育部工程研究中心,重庆400065 [3]移动通信技术重庆市重点实验室,重庆400065

出　　处：《激光与光电子学进展》2023年第10期385-394,共10页Laser & Optoelectronics Progress

基　　金：重庆市教委科学技术研究项目(KJ1400416)。

摘　　要：传统非负矩阵分解(NMF)应用于高光谱解混时,容易受到椒盐噪声的干扰,造成解混的失败。以往的稀疏解混需要在涉及信息比较分散且易受噪声影响的空间域中寻找最优特征子集。为了解决这些问题,提出了基于空谱约束的加权稀疏柯西非负矩阵分解(SSCNMF)算法,首先采用基于柯西损失函数的NMF模型,其在抑制极端异常值方面,有着良好的鲁棒性。其次,引入自适应稀疏权重因子,提高了丰度矩阵的稀疏性。同时,加入光谱空间约束项,其中光谱因子用于测量不同光谱之间的丰度稀疏度,空间因子利用了丰度空间域的平滑性,提高了数据特征的提取效率。分别对模拟数据集和真实数据集进行了仿真实验,通过与一些经典高光谱解混算法的对比,验证了SSCNMF算法的有效性和优良的抗噪声性能。Traditional nonnegative matrix factorization(NMF)applied to hyperspectral unmixing is susceptible to the interference of pretzel noise,resulting in unmixing failure.Previous sparse unmixing requires determining the optimal feature subset in a spatial domain involving more dispersed information and susceptibility to noise.The weighted sparse Cauchynonnegative matrix factorization(SSCNMF)algorithm based on the spatialspectral constraints is proposed to solve these problems.First,the Cauchy lossfunctionbased NMF model,which exhibits excellent robustness in suppressing extreme outliers,is applied.Second,an adaptive sparse weighting factor is introduced to improve the sparsity of the abundance matrix.A spatialspectral constraint term is added,in which the spectral factor is used to measure the sparsity of abundance among different spectra.The spatial factor exploits the smoothness of the spatial domain of abundance to improve the extraction efficiency of data features.Simulation experiments were conducted on simulated and actual datasets.The effectiveness and excellent antinoise performance of the SSCNMF algorithm are verified by comparing it with some classical hyperspectral unmixing algorithms.

关 键 词：遥感与传感器 高光谱解混 非负矩阵分解 柯西损失函数 稀疏 空谱约束 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]