Aiming at the problems of initialization and "local minima" of Non-negative Matrix Factorization (NMF) in hyperspectral unmixing, a spatial correlation constrained NMF linear unmixing algorithm based on Markov Random Field (MRF) (MRF-NMF) was proposed. Firstly, the number of endmembers was estimated by Hyperspectral Signal identification by minimum error (HySime) method, the endmember matrix and abundance matrix were initialized by Vertex Component Analysis (VCA) and Fully Constrained Least Squares (FCLS). Secondly, the energy function of depicting the spatial distribution characteristics of ground objects was established by using MRF to depict the spatial correlation distribution features of ground objects. Finally, the spatial correlation constraint function based on MRF and the NMF standard objective function were used for unmixing in the form of alternating iteration, and the endmember information and abundance decomposition results of hyperspectral data were obtained. The theoretical analysis and experimental results of real data show that, with hyperspectral data of low spatial correlation, compared with the three reference algorithms of Minimum Volume Constrained NMF(MVC-NMF), Piecewise Smoothness NMF with Sparseness Constraints (PSNMFSC) and NMF with Alternating Projected Subgradients (APS-NMF), the endmember decomposition precision of MRF-NMF increases by 7.82%, 12.4% and 10.1%, and the abundance decomposition precision of MRF-NMF increases by 8.34%, 12.6% and 9.87%. The proposed MRF-NMF can make up for NMF's deficiency in depicting spatial correlation features, and reduce the spatial energy distribution error of ground objects.

In heterogeneous networks, the result of network selection should come out according to subjective factors and objective factors, i.e. the users preferences and network objective attributes. But in limited scheme multi-objective decision-making of comprehensive evaluation method, Analytic Hierarchy Process (AHP) has the characteristics of strong subjectivity, the characteristics of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is true, intuitive, and reliable. Therefore, concerning the features of the two methods, in the network selection issue, the user preference aspects in the choice of AHP method, network status using TOPSIS method, a kind of user preferences and network status collaborative judgment network selection algorithm was puts forward. The simulation results show that, without considering load conditions, the proposed algorithm can effectively choose the suitable network for mobile terminal current business.