Multiresolution-based image fusion with additive wavelet decomposition

The standard data fusion methods may not be satisfactory to merge a high-resolution panchromatic image and a low-resolution multispectral image because they can distort the spectral characteristics of the multispectral data. The authors developed a technique, based on multiresolution wavelet decompo...

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Detalles Bibliográficos
Autores: Núñez de Murga, Jorge, 1955-, Otazu Porter, Xavier, Fors Aldrich, Octavi, Prades, Albert, Palà, Vicenç, Arbiol, Román
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:1999
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/8545
Acceso en línea:https://hdl.handle.net/2445/8545
Access Level:acceso abierto
Palabra clave:Geofísica
Teledetecció
Processament d'imatges
Geophysical signal processing
Geophysical techniques
Image processing
Image resolution
Multidimensional signal processing
Remote sensing
Sensor fusion
Terrain mapping
Wavelet transforms
Descripción
Sumario:The standard data fusion methods may not be satisfactory to merge a high-resolution panchromatic image and a low-resolution multispectral image because they can distort the spectral characteristics of the multispectral data. The authors developed a technique, based on multiresolution wavelet decomposition, for the merging and data fusion of such images. The method presented consists of adding the wavelet coefficients of the high-resolution image to the multispectral (low-resolution) data. They have studied several possibilities concluding that the method which produces the best results consists in adding the high order coefficients of the wavelet transform of the panchromatic image to the intensity component (defined as L=(R+G+B)/3) of the multispectral image. The method is, thus, an improvement on standard intensity-hue-saturation (IHS or LHS) mergers. They used the ¿a trous¿ algorithm which allows the use of a dyadic wavelet to merge nondyadic data in a simple and efficient scheme. They used the method to merge SPOT and LANDSATTM images. The technique presented is clearly better than the IHS and LHS mergers in preserving both spectral and spatial information.