Unsupervised Hyperspectral Image Analysis with Projection Pursuit and MRF Segmentation Approach

This work deals with hyperspectral image analysis in the absence of ground-truth. The method adopts a projection pursuit (PP) procedure with entropy index to reduce the dimensionality followed by Markov Random Field (MRF) model based segmentation. 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. The segmented output so obtained is labeled with desired number of landcover classes in the absence of ground-truth. While comparing with original hyperspectral image the methodology outperforms principal component analysis with respect to class separation as exhibited in the illustration of an archive EO-1 hyperspectral image. The technique is not a computational intensive as is usually the case in hyperspectral image analysis. When training samples are available, the segmented regions yields a classified image with any cluster validation technique