High Spatial Resolution Remote Sensing Imagery Classification Based on Markov Random Field Model Integrating Granularity and Semantic Features

In remote sensing image classification, it is difficult to distinguish the homogeneity of same land class and the heterogeneity between different land classes. Moreover, high spatial resolution remote sensing images often show the phenomenon of ground object classes fragmentation and salt-and-pepper noise after classification. To improve the above phenomenon, Markov random field (MRF) is a widely used method for remote sensing image classification due to its effective spatial context description. Some MRF-based methods capture more image information by building interaction between pixel granularity and object granularity. Some other MRF-based methods construct representations at different semantic layers on the image to extract the spatial relationship of objects. This paper proposes a new MRF-based method that combines multi-granularity and different semantic layers of information to improve remote sensing image classification. A hierarchical interaction algorithm is proposed that iteratively updates information between different granularity and semantic layers to generate results. The experimental results demonstrate that: on the Gaofen-2 imagery, the proposed model shows a better classification performance than other methods.