This paper presents a semi-supervised hyperspectral unmixing solution that integrate the spatial information in the abundance estimation procedure. The proposed method is applied on a nonlinear model based on polynomial postnonlinear mixing model where characterizes each pixel reflections composed of nonlinear function of pure spectral signatures added by noise. We partitioned the image to classes where contains similar materials so share the same abundance vector. The spatial correlation between pixels belonging to each class is modelled by Markov Random Field. A Bayesian framework is proposed to estimate the classes and corresponding abundance vectors alternatively. We proposed sparse Dirichlet prior for abundance vector that made it possible to use this algorithm in semisupervised scenario where the exact involved materials are unknown. In this approach, we just need to have a large library of pure spectral signatures including the desired materials. An MCMC algorithm is used to estimate the abundance vector based on generated samples. The result of implementation on simulated data shows the prominence of proposed approach.
The location of the base tranceiver station (BTS) antennas plays important role in the proper service and coverage of the mobile connection in each region. Proper location of these antennas is a major challenge for operators in each country, as in addition to maximum network coverage, service costs must also be acceptable and competitive. This means that in busy areas, in order to provide better service, the antennas must be greater and closer to each other. In general, the location problem is a type of optimization problem that aims to select a subset of the candidate locations to create the facilities that provide the best service at the lowest cost. To solve such problems in a reasonable time, we can use meta-heuristic algorithms to find solutions that are close to the optimal solution. Accordingly, this paper attempts to apply the genetic algorithm (GA) to find a suitable solution for finding BTS mobile antennas in north Kermanshah. To this end, a GA model is proposed that improves the location coordinates of the current BTS antennas extracted from the Geographic Information System (GIS). Comparison of model results with the status of BTS active antennas in Kermanshah shows the performance of the model.
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