Fuzzy connectedness road extraction from high resolution remote sensing image based on GMM-MRF

This paper presents a novel method for extracting road from high resolution remote sensing image based on Gaussian mixture model and Markov random field (GMM-MRF) model optimized by the iterated conditional model (ICM). In this paper, we first divide remote sensing image into 3 classes by GMM-MRF texture segmentation model, and then extract road by fuzzy connectedness. Finally, we use shape features and mathematical morphology to do the post-processing to remove false alarm area. Since fuzzy connectedness lacks regional structure factors, it can not accurately describe the pixel similarity. By introducing image texture feature to the concept of fuzzy connectedness, we can establish texture fuzzy connectedness concept to make it more accurate description of the degree of association among pixels, which reflects the characteristics of the local and regional pixel similarity. Besides, our method choose only one road seed point and can achieve better road extraction effectiveness than fuzzy connectedness without using segmentation algorithms, which usually needs more than 15 seed points. Experiments show that the proposed method can effectively eliminate the non-road areas that whose characteristics are similar to road, and it can obtain better performance for road extraction from high resolution remote sensing image compared with fuzzy c-means and mathematical morphology.