Real-time segmentation of remote sensing images with a combination of clustering and Bayesian approaches

In the area of remote sensing image processing, accurate segmentation of high-resolution remote sensing images in real time remains a challenging problem and numerous approaches have been developed for the problem. This paper proposes a new unsupervised approach that can efficiently analyze a remote sensing image and provide accurate segmentation results. The approach performs segmentation in three stages. In the first stage, an image is partitioned into blocks of equal sizes. The mean values of the R, G and B components of the pixels in each block are computed to form a feature vector of the block. A preliminary segmentation result is obtained by clustering the feature vectors with a simple clustering algorithm. In the second stage, a Bayesian approach is applied to refine the preliminary segmentation result. In the final stage, a graph-based method is utilized to recognize regions with complex texture structures. The performance of this approach has been tested on a few benchmark datasets, and its segmentation accuracy is compared with that of many state-of-the-art segmentation tools for remote sensing images. The testing results show that the overall segmentation accuracy of the proposed approach is higher than that of the other tools, and real-time analysis suggests that the approach is promising for real-time applications. An implementation of the approach in MATLAB is freely available upon request.