Traditional hyperspectral image classification typically uses raw spectral signatures without considering the spatial characteristics. In this paper, we proposed a novel method for hyperspectral image classification based on morphological attribute profiles. We employed independent component analysis for dimensionality reduction and designed an extended multiple attribute profiles (EMAP) to extract spatial features in ICA-induced subspaces. For accurate classification, we proposed a Bayesian maximum a posteriori formulation that couples EMAPs-based feature extraction for the class-conditional probability with an MRF-based prior. Experimental results show that the proposed method substantially outperforms traditional and state-of-the-art methods tending to result in smoother classification maps with fewer erroneous outliers.