###### Abstract  
This paper develops a novel classification optimization approach integrating class adaptive Markov Random Field (MRF) and fuzzy local information (CAMRF-FLI) for high spatial resolution multispectral imagery (HSRMI). Firstly, the raw classification results, including initial fuzzy memberships and class labels of every pixel, are achieved by a pixel-wise classification method for a given image. Secondly, the class adaptive MRF-based data energy function is developed to integrate class spatial dependency information. Thirdly, a novel spatial energy function integrating fuzzy local information is constructed. Finally, based on the total of data and spatial energies, the raw classification map is regularized by a global minimization of the energy function using its iterated conditional modes (ICM). The effectiveness of CAMRF-FLI is performed by two data sets. The results indicate it can refine the classification map in homogeneous areas, meanwhile, reduce most of the edge blurring artifact, and improve the classification accuracy compared with some conventional approaches.  
classification; Markov Random Field; high spatial resolution multispectral imagery 2018 8 1  
classification result [10]. Among them, the Majority voting (MV) filter is the simplest and most widely CPP method, in which the class of central pixel is classified as the majority class in a sliding window. Unfortunately, high accuracies are obtained at the cost of oversmooth losing image details [11]. Gaussian filter (GS) was proposed to improve MV, in which, the class of the central pixel is determined by weighted class fuzzy memberships based on the Gaussian distance in a local window [12]. However this method neglects the centered pixel's own features. Thus, it may produce oversmoothed results. The object-based voting (OBV) method aims to refine the raw classification result by conducting the MV based on the boundary derived from the image segmentation [13]. However, the effectiveness of OBV depends on performance of segmentation. MRF is a classic CPP method, in which the raw classification map is smoothed through the dependence among neighboring pixels [14,15]. While the traditional MRF often leads to oversmoothed solutions for the equal weighted neighbor' influences and fixed coefficient \(\beta\). To overcome this defect, some enhanced MRF were proposed and proved to be effective in preserving image details to a certain extent [16, 17, 18]. These works had developed more complex spatial priori models to suppress the smoothness effect. And the models can effectively preserve boundaries. Unfortunately, these MRF models involving local discontinuities still exist unexpected and isolated class labels around object boundaries. Besides, the selection of the penalty coefficient \(\beta\) is difficult and time-consuming in these methods. From above discussion, we can find that most of CPP methods can obtain correct classification result in homogenous regions, while a lot of errors frequently occur on boundaries. A more efficient method should be developed to deal with such boundary pixels issues.  
This paper presents a novel classification optimization approach integrating class adaptive Markov Random Field (MRF) and fuzzy local information (CAMRF-FLI) approach for HSRMI classification. The aim is to provide a proper trade-off between oversmooth and spatial regularization to produce homogeneous regions while preserving image details. The CAMRF-FLI is validated through two experiments. Results showed that the CAMRF-FLI was effective in improving classification accuracy.