Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/48691
Título: Automatic feature extraction from multisensorial oceanographic imagery
Autores/as: Marcello, Javier 
Maqués, Ferran
Eugenio, F. 
Clasificación UNESCO: 250616 Teledetección (Geología)
Palabras clave: Image texture analysis
Feature extraction
Image processing
Image analysis
Image edge detection, et al.
Fecha de publicación: 2002
Publicación seriada: IEEE International Geoscience and Remote Sensing Symposium proceedings 
Conferencia: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002)/24th Canadian Symposium on Remote Sensing 
Resumen: The problem of identifying mesoscale structures has been studied using a variety of image processing techniques, mainly, texture analysis, edge detection, mathematical morphology, neural networks and wavelet transform. The foremost difficulties encountered in the preceding approaches are the presence of noise, mainly due to clouds and other atmospheric phenomena; the fact that gradients are weak and provide excess of information; the strong morphological variation that impedes an accurate geometric representation and the absence of a valid analytical model for the structures. In this context, the proposed methodology, due to its region-based nature, overcomes the edge detection inconveniences and obtains the proper structure identification. This automatic technique has been applied to the detection and feature extraction of coastal upwellings and filaments in the northwest African coast and the Alboran Sea using imagery from the AVHRR/2&3, SeaWiFS and MODIS sensors. The system has proven to be very effective and robust in a wide variety of climate conditions.
URI: http://hdl.handle.net/10553/48691
ISBN: 0-7803-7536-X
ISSN: 2153-6996
Fuente: International Geoscience and Remote Sensing Symposium (IGARSS),v. 4, p. 2483-2485
Colección:Actas de congresos
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