Title
A new method for spatial resolution enhancement of hyperspectral images using sparse coding and linear spectral unmixing A new method for spatial resolution enhancement of hyperspectral images using sparse coding and linear spectral unmixing
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
conferenceObject
Publication
Bellingham :Spie-int soc optical engineering ,
Subject
Economics
Physics
Engineering sciences. Technology
Source (journal)
IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XXI
Source (book)
Conference on Image and Signal Processing for Remote Sensing XXI, SEP 21-23, 2015, Toulouse, FRANCE
Volume/pages
9643(2015) , 9 p.
ISSN
0277-786X
Article Reference
UNSP 96430I
ISBN
978-1-62841-853-8
ISI
000367469500017
Carrier
E-only publicatie
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Hyperspectral images (HSI) have high spectral and low spatial resolutions. However, multispectral images (MSI) usually have low spectral and high spatial resolutions. In various applications HSI with high spectral and spatial resolutions are required. In this paper, a new method for spatial resolution enhancement of HSI using high resolution MSI based on sparse coding and linear spectral unmixing (SCLSU) is introduced. In the proposed method (SCLSU), high spectral resolution features of HSI and high spatial resolution features of MSI are fused. In this case, the sparse representation of some high resolution MSI and linear spectral unmixing (LSU) model of HSI and MSI is simultaneously used in order to construct high resolution HSI (HRHSI). The fusion process of HSI and MSI is formulated as an ill-posed inverse problem. It is solved by the Split Augmented Lagrangian Shrinkage Algorithm (SALSA) and an orthogonal matching pursuit (OMP) algorithm. Finally, the proposed algorithm is applied to the Hyperion and ALI datasets. Compared with the other state-of-the-art algorithms such as Coupled Nonnegative Matrix Factorization (CNMF) and local spectral unmixing, the SCLSU has significantly increased the spatial resolution and in addition the spectral content of HSI is well maintained.
E-info
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000367469500017&DestLinkType=RelatedRecords&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000367469500017&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
https://repository.uantwerpen.be/docman/iruaauth/961b5a/131088.pdf
Handle