Classification of Hyperspectral Data over Urban Areas Based on Extended Morphological Profile with Partial Reconstruction

Extended morphological profiles with reconstruction are widely used in the classification of very high resolution hyperspectral data from urban areas. However, morphological profiles constructed by morphological openings and closings with reconstruction can lead to some undesirable effects. Objects expected to disappear at a certain scale remain present when using morphological openings and closings by reconstruction. In this paper, we apply extended morphological profiles with partial reconstruction (EMPP) to the classification of high resolution hyperspectral images from urban areas. We first used feature extraction to reduce the dimensionality of the hyperspectral data, as well as reduce the redundancy within the bands, then constructed EMPP on features extracted by PCA, independent component analysis and kernel PCA for the classification of high resolution hyperspectral images from urban areas. Experimental results on real urban hyperspectral image demonstrate that the proposed EMPP built on kernel principal components gets the best results, particularly in the case with small training sample sizes.