Paper Info
Title
Unsupervised Bayesian Subpixel Mapping of Hyperspectral Imagery Based on Band-Weighted Discrete Spectral Mixture Model and Markov Random Field
Abstract
Although accurate training and initialization information is difficult to acquire, unsupervised hyperspectral subpixel mapping (SPM) without relying on this predefined information is an insufficiently addressed research issue. This letter presents a novel Bayesian approach for unsupervised SPM of hyperspectral imagery (HSI) based on the Markov random field (MRF) and a band-weighted discrete spectral mixture model (BDSMM), with the following key characteristics. First, this is an unsupervised approach that allows adjustment of abundance and endmember information adaptively for less relying on algorithm initialization. Second, this approach consists of the BDSMM for accommodating the noise heterogeneity and the hidden label field of subpixels in HSI. The BDSMM also integrates SPM into the spectral mixture analysis and allows enhanced SPM by fully exploring the endmember-abundance patterns in HSI. Third, the MRF and BDSMM are integrated into a Bayesian framework to use both the spatial and spectral information efficiently, and an expectation-maximization (EM) approach is designed to solve the model by iteratively estimating the endmembers and the label field. Experiments on both simulated and real HSI demonstrate that the proposed algorithm can yield better performance than traditional methods.
Year
DOI
Venue
2021
10.1109/LGRS.2020.2967104
IEEE Geoscience and Remote Sensing Letters
Keywords
DocType
Volume
Band-weighted discrete spectral mixture model (BDSMM),Bayesian framework,hyperspectral image,Markov random field (MRF),unsupervised subpixel mapping (SPM)
Journal
18
Issue
ISSN
Citations 
1
1545-598X
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Qi-ming Qin115849.12
Xu, L.2467.46
Yuan Fang303.38
Junhuan Peng4269.66
Wenfu Yang500.34
Alexander Wong635169.61
D. A. Clausi7768.80