红外与毫米波学报2018，Vol.37Issue(2)：144-153,10.DOI:10.11972/j.issn.1001-9014.2018.02.004
融入结构信息的稀疏低秩丰度估计算法研究
parse and low-rank abundance estimation with structural information
摘要
Abstract
Abundance estimation(AE)plays an essential role in the hyperspectral image processing and analysis. Owing to the simplicity and mathematical tractability, various methods based on the constrained linear regression are usually developed to estimate abundance matrix.The obvious limitation of these approaches is that the fitness between the estimated data and ground-truth data does not include the structural information, e.g.row difference and column difference.In this paper,a novel linear regression algorithm is proposed by jointly adding the multi-structured information to the traditional linear regression model.And it is employed to modify sparse and low-rank abundance estimation model to improve estimated accuracy and robustness.Firstly,a new linear regression model is established by taking into account the structural information.Then,mathematical proof of the new linear regres-sion method is presented.Afterwards,it is applied to modify the sparse low-rank abundance estimation model.Fi-nally,Alternating Direction Method of Multipliers(ADMM)technique is adopted to solve the new model.The ex-perimental results demonstrate that the proposed algorithms can capture structural information and improve the esti-mated performance on the simulated dataset and the real hyperspectral remote sensing images.关键词
解混/稀疏低秩/结构信息/丰度矩阵/交替乘子法(ADMM)Key words
unmixing/sparse and low rank/structural information/abundance matrix/alternating direction meth-od of multipliers(ADMM)分类
信息技术与安全科学引用本文复制引用
袁静,章毓晋,杨德贺..融入结构信息的稀疏低秩丰度估计算法研究[J].红外与毫米波学报,2018,37(2):144-153,10.基金项目
Supported by National Natural Science Foundation of China(61673234,U1636124) （61673234,U1636124）
