A Novel Classification Optimization Approach Integrating Class Adaptive MRF and Fuzzy Local Information for High Spatial Resolution Multispectral Imagery

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.

Keywords

• classification
• Markov Random Field
• high spatial resolution multispectral imagery