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—Hyperspectral imaging involves large amounts of information. This paper presents a technique for dimensionality reduction to deal with hyperspectral images. The proposed method is based on a hierarchical clustering structure to group bands to minimize the intracluster variance and maximize the intercluster variance. This aim is pursued using information(More)
A semi-supervised pixel classification scheme for hyperspectral satellite images is presented. The scheme includes a previous band selection step followed by a clustering process to select modes of interest that will be labeled by an expert. Then pixel classification is performed resulting in a segmentation and classification of the fields appearing in the(More)
—This letter presents a spectral–spatial pixel characterization method for hyperspectral images. The characterization is based on textural features obtained using Gabor filters over a selected set of spectral bands. This scheme aims at improving land-use classification results, decreasing significantly the number of spectral bands needed in order to reduce(More)
Four different texture classification methods (wavelet-based, co-occurrence matrices-based, 1D-histograms-based, and 1D Boolean model-based) are systematically compared and evaluated with respect to their performance in identifying textures from small and irregular samples. Two sets of 135 complex shape masks (symmetric and nonsymmetric) are created using(More)
We generalize here the use of the 1D Boolean model for the analysis of grey level textures. Each grey image is first split into eight binary images using different criteria. Each of these binary images is separately analysed with the help of the 1D Boolean model and features are extracted from it. The final grey texture recognition is performed on the basis(More)
This work presents the application of a novel technique on dimensionality reduction to deal with multispectral images. A distance based on mutual information is used to construct a hierarchical clustering structure. Experimental results show that the method provides a very suitable subset of multispectral bands for pixel classification purposes.
Feature selection and dimensionality reduction are crucial research fields in pattern recognition. This work presents the application of a novel technique on dimensionality reduction to deal with multispectral images. A distance based on mutual information is used to construct a hierarchical clustering structure with the multispectral bands. Moreover, a(More)
In this paper we present an unsupervised algorithm to select the most adequate grouping of regions in an image using a hierarchical clustering scheme. Then, we introduce an optimisation approach for the whole process. The grouping method presented is based on the maximisation of a measure that represents the perceptual decision. The whole strategy takes(More)