Probabilistic Classification of Hyperspectral Images by Learning Nonlinear Dimensionality Reduction Mapping

In this paper, we combined the application of a non-linear dimensionality reduction technique, isomap, with expectation maximisation in graphical probabilistic models for learning and classification of hyperspectral image. Hyperspectral image spectroscopy gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and man-made objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and user-intensive. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a mixture of linear models representation similar to a mixture of factor analysers, the joint probability distributions of the model can be calculated offline. The learnt model is then applied to the hyperspectral image at run-time and data classification can be performed. We also show the comparison with results from standard techniques