L1/2正则化的逐次高光谱图像光谱解混  

Successive spectral unmixing for hyperspectral images based on L1/2 regularization

作　　者：汤毅 粘永健[2] 何密[2] 王倩楠[2] 许可 Tang Yi;Nian Yongjian;He Mi;Wang Qiannan;Xu Ke(Beijing Education Network and Information Center,Beijing 100089,China;School of Biomedical Engineering and Imaging Medicine,Army Medical University (Third Military Medical University),Chongqing 400038 China;College of Electronic Science,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]北京教育网络和信息中心,北京100089 [2]陆军军医大学(第三军医大学)生物医学工程与影像医学系,重庆400038 [3]国防科技大学电子科学学院,湖南长沙410073

出　　处：《红外与激光工程》2019年第7期286-294,共9页Infrared and Laser Engineering

基　　金：重庆市基础科学与前沿技术一般项目(cstc2016jcyjA0539)

摘　　要：由于高光谱遥感图像的混合程度较高,使得传统的非负矩阵欠逼近(Nonnegative Matrix Underapproximation,NMU)算法所提取的基本成分仍然"不纯",且易受噪声影响。针对这些不足,提出了一种基于L1/2正则化的软阈值NMU逐次光谱解混算法。首先,通过引入丰度的L1/2正则项来增强算法的地物区分能力,进而提高所分离地物的纯度;其次,利用软阈值惩罚函数代替NMU中的残差非负约束,通过调节惩罚因子来控制非负元素的数量,从而提高算法的抗噪性能。在仿真数据和实测数据上的实验结果表明,即使在有噪声的条件下,该算法也能得到较好的分离结果。Due to the high mixed degree of hyperspectral remote sensing images,the basic component extracted by the traditional Nonnegative Matrix Underapproximation(NMU)algorithm is still"impurity",moreover,it is susceptible to noise.To overcome the above shortcomings,a method named L1/2-regularized soft-thresholding NMU for hyperspectral unmixing was proposed.Firstly,the L1/2 regularization term for abundance was introduced to improve the distinguishing ability,which can further improve the purity of the extracted components.Secondly,the soft-threshold penalty function was introduced to replace the residual nonnegative constraint in NMU.By adjusting the penalty factor,the number of non-negative elements could be well controlled,which could improve the anti-noise ability.Experimental results on the simulational and real datasets show that the proposed algorithm can obtain better separation results even under noisy conditions.

关 键 词：高光谱遥感 光谱解混 非负矩阵欠逼近 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]