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This paper deals with hyperspectral image classification in remote sensing. The proposed scheme is a spectral-spatial technique based on wavelet transforms and mathematical morphology. The original contribution of this paper is that the extended morphological profile (EMP) is created from the features extracted by wavelets, which has proven to be better or comparable to other techniques for dimensionality reduction of hyperspectral data. In addition, the hyperspectral image is denoised, also using wavelets, with the objective of removing undesirable artifacts introduced in the acquisition of the data. The classification is carried out by a support vector machine (SVM) classifier. The accuracy is improved when comparing with previously developed spectral-spatial SVM-based schemes.