In this study

we put forward an MRF-ν-SVM classification system that integrates spatial contextual information with SVM while taking advantage of the good classification performance of SVM for small samples and its effective capacity to model the spatial correlation of MRFs. The system performs classification at the regional level instead of at the pixel level. To protect the image edge structure

we set up a dual threshold for local edge intensity and refine the MRF-based spatial contextual model to a contextual model for fuzzy edges. The bias of the original problems of SVM is then optimized to improve the optimal hyperplane.Sea ice is an important component of the global climate system. As a unique coherent imaging system

the Synthetic Aperture Radar（ SAR） provides high-resolution images and serves as an important tool for monitoring sea ice. Sea ice image classification via SAR is thus an effective method for monitoring sea ice. This study integrates the spatial correlation based on MRF into the support vector classifier. Using the spatial contextual information based on MRF can improve scene interpretation accuracy. Meanwhile

SVM has good generalization ability and efficient classification performance

as well as high robustness to the Hughes phenomenon. MRF is thus used to model space interaction in the SVM framework.The watershed segmentation algorithm is used to obtain a number of homogeneous areas and the edges of closed regions. The image is then classified at the regional level. We extract the gray tone and three GLCM texture statistics

namely

entropy

contrast

and correlation

as the regional feature vector of the samples. For the characteristics of the sea ice image obtained via SAR

we use a window of a certain size to collect training samples and to record the edge information of each window image. The spatial contextual model based on the neighborhood system is optimized to the contextual model of the edge

which is introduced to the bias of the SVM

to structure the contextual bias of the edge and then correct the optimal hyperplane. To improve classification accuracy

we set up a double threshold for the strength of the edge. The labels of the regions for the strong edge are reserved

the edge label for the weak edge is cleared

and the contextual information for the fuzzy edge is computed.When the spatial correlation of images is not considered

several speckle-like regions can be observed in the classification maps of the KNN and ν-SVC algorithms. The regional MRF classification demonstrates weak noise resistance

and the classification result of the post SVM-MRF algorithm cannot accurately identify the regions with similar features. Experiments show that the proposed algorithm can effectively improve classification accuracy

enhance speckle noise resistance

and accurately classify the regions with similar features but different labels.The MRF-ν-SVM classification system that integrates spatial contextual information with SVM can effectively classify SAR images. The proposed algorithm has more advantages than the other four algorithms tested in the study. Specifically

it can accurately classify SAR images

reduce interference from speckle noise

and recognize regions with similar features but different labels.