Model Selection in Markov Random Fields for High Spatial Resolution Hyperspectral Data

2009 ◽  
pp. 545-563
Author(s):  
Francesco Lagona
Keyword(s):  
Model Selection ◽  
Spatial Resolution ◽  
Random Fields ◽  
Markov Random Fields ◽  
High Spatial Resolution ◽  
Hyperspectral Data ◽  
Markov Random

Spectral-spatial destriping of hyperspectral image via correntropy based sparse representation and unidirectional Huber–Markov random fields

2017 ◽  
Vol 15 (06) ◽  
pp. 1750056 ◽  
Author(s):  
Yulong Wang ◽  
Yuan Yan Tang ◽  
Luoqing Li
Keyword(s):  
Sparse Representation ◽  
Random Fields ◽  
Markov Random Fields ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Data Sets ◽  
Spectral Correlation ◽  
Markov Random ◽  
Augmented Lagrange Multiplier

This paper presents a novel destriping method for hyperspectral images. Most of the previous destriping methods regard only the corrupted subimage as an isolated image and fail to consider the high spectral correlation between the subimages in different bands. This may impede their performance of removing striping noises. The proposed method takes advantage of both spectral and spatial information to contribute to the process of striping noise reduction. Firstly, a correntropy-based sparse representation (CSR) model is utilized to learn the high spectral correlation between the subimages in different bands. Then the spatial information of the target subimage with striping noise is incorporated into the CSR model with a unidirectional Huber–Markov random field prior. We devise an Augmented Lagrange Multiplier type of algorithm to efficiently compute the destriped results. The experimental results on two real-world hyperspectral data sets demonstrate the effectiveness of the proposed method.

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