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Several spatial features are compared for the spatial/spectral classification of hyperspectral data. These features are extracted from texture spectra, co-occurrence matrices and morphological profiles. First, a PCA (Principal Components Analysis) is carried out on the hyperspectral image and textural features are computed on the first principal components. These textural features are concatenated...
A method is proposed for the classification of hyperspectral data with high spatial resolution by Support Vector Machine (SVM) with multiple kernels. The approach is an extension of previous sole-kernel classifiers by integrating spectral features with spatial or structural features for hyperspectral classification. Using Support Vector Machine (SVM) as the classifier, different multi-kernel SVM classifiers...
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