scholarly journals Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation

2017 ◽  
Vol 9 (6) ◽  
pp. 541 ◽  
Author(s):  
Elham Ghasrodashti ◽  
Azam Karami ◽  
Rob Heylen ◽  
Paul Scheunders
Keyword(s):  
Sparse Representation ◽  
Spatial Resolution ◽  
Resolution Enhancement ◽  
Spectral Unmixing ◽  
Hyperspectral Images

A Hardware Accelerator for Onboard Spatial Resolution Enhancement of Hyperspectral Images

2020 ◽  
pp. 1-5
Author(s):  
Felipe Viel ◽  
Wemerson Delcio Parreira ◽  
Altamiro Amadeu Susin ◽  
Cesar Albenes Zeferino
Keyword(s):  
Spatial Resolution ◽  
Resolution Enhancement ◽  
Hyperspectral Images ◽  
Hardware Accelerator

scholarly journals Fusion of Hyperspectral and Multispectral Images Based on a Centralized Non-local Sparsity Model of Abundance Maps

Tecnura ◽  
2020 ◽  
Vol 24 (66) ◽  
pp. 62-75
Author(s):  
Edwin Vargas ◽  
Kevin Arias ◽  
Fernando Rojas ◽  
Henry Arguello
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Spatial Resolution ◽  
Real Data ◽  
Spectral Unmixing ◽  
Multispectral Images ◽  
Self Similarity ◽  
Diverse Range ◽  
Learned Dictionary ◽  
Non Local

Objective: Hyperspectral (HS) imaging systems are commonly used in a diverse range of applications that involve detection and classification tasks. However, the low spatial resolution of hyperspectral images may limit the performance of the involved tasks in such applications. In the last years, fusing the information of an HS image with high spatial resolution multispectral (MS) or panchromatic (PAN) images has been widely studied to enhance the spatial resolution. Image fusion has been formulated as an inverse problem whose solution is an HS image which assumed to be sparse in an analytic or learned dictionary. This work proposes a non-local centralized sparse representation model on a set of learned dictionaries in order to regularize the conventional fusion problem.Methodology: The dictionaries are learned from the estimated abundance data taking advantage of the depth correlation between abundance maps and the non-local self- similarity over the spatial domain. Then, conditionally on these dictionaries, the fusion problem is solved by an alternating iterative numerical algorithm.Results: Experimental results with real data show that the proposed method outperforms the state-of-the-art methods under different quantitative assessments.Conclusions: In this work, we propose a hyperspectral and multispectral image fusion method based on a non-local centralized sparse representation on abundance maps. This model allows us to include the non-local redundancy of abundance maps in the fusion problem using spectral unmixing and improve the performance of the sparsity-based fusion approaches.

scholarly journals Spectral Unmixing for the Classification of Hyperspectral Images at a Finer Spatial Resolution

2011 ◽  
Vol 5 (3) ◽  
pp. 521-533 ◽  
Author(s):  
Alberto Villa ◽  
Jocelyn Chanussot ◽  
Jón Atli Benediktsson ◽  
Christian Jutten
Keyword(s):  
Spatial Resolution ◽  
Spectral Unmixing ◽  
Hyperspectral Images
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