ABSTRACT:The main idea of this paper is to integrate the non-contextual support vector machines (SVM) classifiers with Markov random fields (MRF) approach to develop a contextual framework for monitoring of agricultural land cover. To this end, the SVM and MRF approaches were integrated to exploit both spectral and spatial contextual information in the image for more accurate classification of remote sensing data from an agricultural region in Biddinghuizen, the Netherlands. Comparative analysis of this study clearly demonstrated that the proposed contextual method based on SVM-MRF models generates a higher average accuracy, overall accuracy and Kappa coefficient compared with non-contextual SVM method. Since the spatial information is considered in the proposed method, this study indicates that a neater, more homogonous and speckle-free results could be generated by the SVM-MRF approach.

In classical uncapacitated facility location problems (UFLP) the goal is to satisfy requirements of some demand points by setting up some servers, among potential facility locations, such that the total cost including service costs and fixed costs are minimized. In this paper a generalization of UFLP is considered in which some demand points, called self-serving, could be served exclusively by a new server at that point. Numerical experiments show that near optimal solutions are achieved by the proposed method.