Abstract
A hybrid approach for hyperspectral image segmentation is presented in this paper. The contribution of the proposed work is in two folds. First, learning of the class posterior probability distributions with Quadratic Programming or joint probability distribution by employing sparse multinomial logistic regression (SMLR) model. Secondly, estimation of the dependencies using spatial information and edge information by minimum spanning forest rooted on markers by acquiring the information from the first step to segment the hyper spectral image using a Markov Random field segments. The particle swarm optimization (PSO) is performed based on the SMLR posterior probabilities to reduce the large number of training data set. The performance of the proposed approach is illustrated in a number of experimental comparisons with recently introduced hyperspectral image analysis methods using both simulated and real hyper spectral data sets of Mars.
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Karchi, R.P., Nagesh, B.K. (2017). A Hybrid Approach for Hyper Spectral Image Segmentation Using SMLR and PSO Optimization. In: Santosh, K., Hangarge, M., Bevilacqua, V., Negi, A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_24
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DOI: https://doi.org/10.1007/978-981-10-4859-3_24
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