Segmentation of Remotely Sensed Images using Unsupervised ‘Sampling-Resampling’ based on Hopfield Type Neural Network (International Journal of Engineering and Computer Science, 2018)
In this paper we propose a technique for performing unsupervised segmentation for satellite images using a ’sampling – resampling’ based on Hopfield type Neural Network. The multi band values of the satellite images are grouped into clusters that are modeled using Gaussians. The parameters of Gaussian mixture models are learnt using Hopfield Type Neural Network. The purpose of this work is to show the effectiveness of the results obtained by using Hopfield type Neural Network rather than Bayesian parameter estimation. Each spatial position in the considered image is represented by neuron that is connected only to its neighboring units. It can be observed that the proposed technique have a better correspondence to the actual land features in the satellite images than compared with the results obtained by using the clustering technique like K-means Algorithm.  The unsupervised techniques learns the class parameter by exploiting the structure of the unlabeled data .However ,the numerical integration technique that are required for implementing Bayesian learning becomes complicated for practical applications, because of involving large data’s than compared to the Hopfield type Neural Network model.