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Support Vector Machine Fusion of Multisensor Imagery in Tropical Ecosystems

Domaine:

geospatial

Type de record:

paper
Créateur:
Pouteau, RobinStoCha
Éditeur:
Uni
Éditeur:
CCSD
Hôte:avatar
International audience One of the major stakeholders of image fusion is being able to process the most complex images at the finest possible integration level and with the most reliable accuracy. The use of support vector machine (SVM) fusion for the classification of multisensors images representing a complex tropical ecosystem is investigated. First, SVM are trained individually on a set of complementary sources: multispectral, synthetic aperture radar (SAR) images and a digital elevation model (DEM). Then a SVM-based decision fusion is performed on the three sources. SVM fusion outperforms all monosource classifications outputting results with the same accuracy as the majority of other comparable studies on cultural landscapes. SVM-based hybrid consensus classification does not only balance successful and misclassified results, it also uses misclassification patterns as information. Such a successful approach is partially due to the integration of DEM-extracted indices which are relevant to land cover mapping in non-cultural and topographically complex landscapes.

Visit

hal.science

Tasks

computer visionimage classification

Tags

Digital elevation model (DEM)Synthetic aperture radar (SAR) imageMultispectral imageLand cover mappingImage fusion---Support vector machines (SVM)[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing

Licenses

info:eu-repo/semantics/OpenAccess

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