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This work presents a classification technique for hyperspectral image analysis when concurrent ground truth is either unavailable or available. The method adopts a principal component analysis (PCA)-based projection pursuit (PP) procedure with an entropy index for dimensionality reduction, followed by a Markov random field (MRF) model-based segmentation. An ordinal optimization approach to PP determines a set of ‘good enough projections’ with high probability, the best among which is chosen with the help of MRF model-based segmentation. When ground-truth is absent, the segmented output obtained is labelled with the desired number of classes so that it resembles the natural scene closely. When the land-cover classes are in detailed level, some special reflectance characteristics based on the classes of the study area are determined and incorporated in the segmentation stage. Segments are evaluated …