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The accuracy of rainforests classification is generally improved by the input of multisensory data since complex vegetation type identification benefits from complementary information. However, in some cases, multisource fusion can also deteriorate accuracy when irrelevant sources are added. Thus, we introduce a fusion method for classes “in difficulty”. Our method outperforms the classical global...
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,...
Researches on land cover classification have a complete lack of ground truth methodology description. We propose a method to track ecotones as privileged training areas for SVM-based natural vegetation classification. This guidance method combines (i) the construction of a principal component analysis (PCA) on spectral bands and gray level co-occurence matrix texture attributes calculated on very...
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