MULTI-RESOLUTION SPATIAL UNMIXING FOR MERIS AND LANDSAT IMAGE FUSION

Nowadays, the increasing quantity of applications using images from Earth Observation satellites makes demanding better spatial, spectral and temporal resolutions. Nevertheless, due to the technical constraint of a trade off between spatial and spectral resolutions, and between spatial resolution an...

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Main Authors: Amoros-Lopez, J, Gomez-Chova, L, Guanter, L, Alonso, L, Moreno, J, Camps-Valls, G, IEEE
Format: Conference item
Published: 2010
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author Amoros-Lopez, J
Gomez-Chova, L
Guanter, L
Alonso, L
Moreno, J
Camps-Valls, G
IEEE
author_facet Amoros-Lopez, J
Gomez-Chova, L
Guanter, L
Alonso, L
Moreno, J
Camps-Valls, G
IEEE
author_sort Amoros-Lopez, J
collection OXFORD
description Nowadays, the increasing quantity of applications using images from Earth Observation satellites makes demanding better spatial, spectral and temporal resolutions. Nevertheless, due to the technical constraint of a trade off between spatial and spectral resolutions, and between spatial resolution and coverage, high spatial resolution is related with low spectral and temporal resolutions and vice versa. Data fusion methods are a good solution to combine information from multiple sensors in order to obtain image products with better characteristics. In this paper, we propose an image fusion approach based on a multi-resolution and multi-source unmixing. The proposed methodology yields a composite image with the spatial resolution of the higher resolution image (downscaling) while retaining the spectral and temporal characteristics of the medium spatial resolution image. The approach is tested in the specific cases of ENVISAT/MERIS and Landsat/TM instruments, but is general enough to be applied to other sensor combination. © 2010 IEEE.
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spelling oxford-uuid:0d504c7c-3931-401c-ae0c-9ad40473abb12022-03-26T09:39:50ZMULTI-RESOLUTION SPATIAL UNMIXING FOR MERIS AND LANDSAT IMAGE FUSIONConference itemhttp://purl.org/coar/resource_type/c_5794uuid:0d504c7c-3931-401c-ae0c-9ad40473abb1Symplectic Elements at Oxford2010Amoros-Lopez, JGomez-Chova, LGuanter, LAlonso, LMoreno, JCamps-Valls, GIEEENowadays, the increasing quantity of applications using images from Earth Observation satellites makes demanding better spatial, spectral and temporal resolutions. Nevertheless, due to the technical constraint of a trade off between spatial and spectral resolutions, and between spatial resolution and coverage, high spatial resolution is related with low spectral and temporal resolutions and vice versa. Data fusion methods are a good solution to combine information from multiple sensors in order to obtain image products with better characteristics. In this paper, we propose an image fusion approach based on a multi-resolution and multi-source unmixing. The proposed methodology yields a composite image with the spatial resolution of the higher resolution image (downscaling) while retaining the spectral and temporal characteristics of the medium spatial resolution image. The approach is tested in the specific cases of ENVISAT/MERIS and Landsat/TM instruments, but is general enough to be applied to other sensor combination. © 2010 IEEE.
spellingShingle Amoros-Lopez, J
Gomez-Chova, L
Guanter, L
Alonso, L
Moreno, J
Camps-Valls, G
IEEE
MULTI-RESOLUTION SPATIAL UNMIXING FOR MERIS AND LANDSAT IMAGE FUSION
title MULTI-RESOLUTION SPATIAL UNMIXING FOR MERIS AND LANDSAT IMAGE FUSION
title_full MULTI-RESOLUTION SPATIAL UNMIXING FOR MERIS AND LANDSAT IMAGE FUSION
title_fullStr MULTI-RESOLUTION SPATIAL UNMIXING FOR MERIS AND LANDSAT IMAGE FUSION
title_full_unstemmed MULTI-RESOLUTION SPATIAL UNMIXING FOR MERIS AND LANDSAT IMAGE FUSION
title_short MULTI-RESOLUTION SPATIAL UNMIXING FOR MERIS AND LANDSAT IMAGE FUSION
title_sort multi resolution spatial unmixing for meris and landsat image fusion
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AT guanterl multiresolutionspatialunmixingformerisandlandsatimagefusion
AT alonsol multiresolutionspatialunmixingformerisandlandsatimagefusion
AT morenoj multiresolutionspatialunmixingformerisandlandsatimagefusion
AT campsvallsg multiresolutionspatialunmixingformerisandlandsatimagefusion
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