Land Cover Classification Using Hidden Markov Models

This paper, proposed a classification approach that utilizes the high recognition ability of Hidden Markov Models (HMM s) to perform high accuracy of classification by exploiting the spatial inter pixels dependencies ( i.e. the context ) as well as the spectral information. Applying unsupervised classification to remote sensing images can provide benefits in converting the raw image data into useful information which achieves high classification accuracy. It is known that other clustering schemes as traditional k-means does not take into account the spatial inter-pixels dependencies. Experiments work has been conducted on a set of 10 multispectral satellite images. Proposed algorithm is verified to simulate images and applied to a selected satellite image processing in the MATLAB environment.