期刊论文

Mei, Xiaoguang

英文

Remote Sensing

2072-4292

2019

11

8

911

10.3390/rs11080911

National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61805181, 61705170, 61605146]

本校为其他机构

Gaussian mixture model (GMM) has been one of the most representative models for hyperspectral unmixing while considering endmember variability. However, the GMM unmixing models only have proper smoothness and sparsity prior constraints on the abundances and thus do not take into account the possible local spatial correlation. When the pixels that lie on the boundaries of different materials or the inhomogeneous region, the abundances of the neighboring pixels do not have those prior constraints. Thus, we propose a novel GMM unmixing method based on superpixel segmentation (SS) and low-rank rep...