激光与光电子学进展, 2020, 57 (24): 241010, 网络出版: 2020-12-30
基于最小体积稀疏正则的高光谱解混方法的研究 下载: 895次
Hyperspectral Unmixing Method Based on Minimum Volume Sparse Regularization
摘要
高光谱解混的目的在于提取图像中的端元特征和丰度特征。由于高光谱图像空间分辨率低而存在大量混合像元,因此如何从混合像元中提取光谱特征和空间分布信息是高光谱解混面临的难题。基于非负矩阵分解的高光谱解混是一个不适定拟合问题,而且在处理过程中将立方体数据转化为矩阵会导致三维结构信息的丢失。利用最小体积单纯形空间稀疏性,提出一种基于最小体积稀疏正则的高光谱解混方法,能够挖掘出图像中光谱特性和丰度特征的内在关系,减少结构信息的丢失。将凸几何中的最小体积约束与非负矩阵分解相结合,并采用近似交替优化与交替方向乘子法设计出高效的求解算法。最后分别采用合成数据和真实数据进行仿真实验,结果表明该种算法能够有效地提取出高光谱图像的端元特征和丰度特征。
Abstract
Hyperspectral unmixing aims to extract the endmember and abundance features in an image. A hyperspectral image has many mixed pixels because of the low spatial resolution. Therefore, capturing the spectral features and the corresponding spatial distribution from the mixed pixels is important. The non-negative matrix factorization(NMF)-based method for hyperspectral unmixing is regarded as an ill-posed data-fitting problem, in which the cube data must be converted into a matrix form, which leads to the loss of three-dimensional structure information. This study introduces the sparsity of the spatial features in the minimum-volume simplex to propose a novel method for hyperspectral unmixing, which not only mines the intrinsic relationship between spectral and spatial abundance features in the images, but also improves the loss of data structure information. The proximal alternating optimization and the alternating direction method of multipliers were used here to design a set of efficient solvers based on the minimum volume constraint in convex geometry and non-negative matrix decomposition. After testing the synthesized and real data sets, the experimental results show that the proposed algorithm can effectively extract the endmember and abundance features.
徐光宪, 王延威, 马飞, 杨飞霞. 基于最小体积稀疏正则的高光谱解混方法的研究[J]. 激光与光电子学进展, 2020, 57(24): 241010. Guangxian Xu, Yanwei Wang, Fei Ma, Feixia Yang. Hyperspectral Unmixing Method Based on Minimum Volume Sparse Regularization[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241010.