高光谱数据截断加权核范数稀疏解混  

Sparse unmixing with truncated weighted nuclear norm for hyperspectral data

作　　者：李璠[1] 张绍泉 曹晶晶[2] 梁炳堃 李军[2] 刘凯[2] 邓承志[1] 汪胜前[1] LI Fan;ZHANG Shaoquan;CAO Jingjing;LIANG Bingkun;LI Jun;LIU Kai;DENG Chengzhi;WANG Shengqian(Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing,Nanchang Institute of Technology,Nanchang 330099,China;School of Geography and Planning,Sun Yat-Sen University,Guangzhou 510275,China)

机构地区：[1]南昌工程学院江西省水信息协同感知与智能处理重点实验室,南昌330099 [2]中山大学地理科学与规划学院,广州510275

出　　处：《遥感学报》2022年第6期1067-1082,共16页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:62141105,61901208,61865012,61771496);中国博士后科学基金(编号:2020M672483);中山大学中央高校基本科研业务费专项(编号:2021qntd22);江西省重点研发计划(编号:20181ACG70022,20202BBGL73081)。

摘　　要：受仪器和观测条件限制,高光谱数据易受噪声污染,给数据解译带来挑战。针对传统稀疏解混模型抗噪性能差的问题,本文提出一种截断加权核范数稀疏解混方法,利用高光谱图像像元之间的相关性减轻噪声对丰度估计的干扰。该方法借助低秩表示在挖掘数据内在低维结构方面的优势,在稀疏解混中加入基于截断加权核范数的低秩约束,并结合加权稀疏技术,在稀疏正则项中引入空间邻域权重。截断加权核范数对丰度矩阵的奇异值向量分段处理,可以更好地实现丰度矩阵的低秩逼近,使丰度图像保持空间一致性并保留更多细节信息,空间加权策略则增强了丰度图像的空间连续性。模拟高光谱数据、Cuprite矿区真实数据和红树林高光谱数据实验表明,与其他先进的稀疏解混方法相比,所提方法具有更好的抗噪性,能够提高解混精度。Spectral unmixing is an important technology for quantitative analysis of hyperspectral images,which estimates the pure source signal(endmember)and the corresponding fractional proportion(abundance).Sparse unmixing is one of the research highlights in the field of spectral unmixing.Sparse unmixing finds a set of endmembers that can optimally model mixed pixels from a known spectral library and takes the fractional abundance as the weight,thereby circumventing the process of endmember extraction.However,hyperspectral data are often contaminated by noise due to the limitations of instruments and observation conditions.This state is disadvantageous to data interpretation.Sparse unmixing is peculiarly prone to be disturbed by noise and thus affect the accuracy of abundance estimation or even erroneously identify endmembers from spectral libraries.To overcome this drawback,this study proposes a hyperspectral sparse unmixing method with truncated weighted nuclear norm,which exploits the correlation of pixels to reduce the interference of noise on abundance estimation.The proposed method adds the low-rank constraint based on truncated weighted nuclear norms to the sparse unmixing model given that low-rank representation is available to mine the inherent low-dimensional structure of data.It is different from other nuclear norm minimization,singular values are divided into two groups and treated with the truncated nuclear norm and weighted kernel norm.It provides a better low-rank approximation of the abundance matrix,which maintains the spatial consistency of image and protects the detailed information.Inspired by the weighted sparse strategy,the spatial neighborhood weight is introduced into the sparse regularization term,which enhances the spatial continuity of image.The underlying optimization problem is solved by the alternating direction method of multipliers efficiently.Experiments are conducted on simulated data,real Cuprite,and mangrove hyperspectral data to verify the unmixing performance of the algorithm.In par

关 键 词：遥感 高光谱数据 稀疏解混 低秩正则化 截断加权核范数 空间权重 

分 类 号：P237[天文地球—摄影测量与遥感] TP751[天文地球—测绘科学与技术]