Spectral-Spatial Classification of Hyperspectral Data Using 3-D Morphological Profile

Biao Hou, Taimin Huang, Licheng Jiao

Abstract: A new spectral-spatial method based on a 3-D morphological profile (3D-MP) is proposed for hyperspectral data classification. As an extension of a previous approach, the proposed method uses both the spectral and spatial information for classification. First, random projection (RP) is used for dimensionality reduction of hyperspectral data. After RP in spectral domain, a novel 3D-MP method is proposed to exploit the dependence between data. Finally, the classification is performed by the widely used support vector machine classifier. Our experiments reveal that the proposed approach exploits the 3-D spectral-spatial feature to provide the state-of-the-art classification results for different hyperspectral data sets.