Large Scale Remote Sensing Image Segmentation Based on Fuzzy Region Competition and Gaussian Mixture Model

Abstract

With the ever-increasing amount and complexity of remote sensing image data, the development of large-scale image segmentation analysis algorithms has not kept pace with the need for methods that improve the final accuracy of object recognition. In particular, the development of such methods for large-scale images poses a considerable challenge. Traditional image segmentation methods are of high time complexity and low segmentation accuracy. This paper presents a new large-scale remote sensing image segmentation method that combines fuzzy region competition and the Gaussian mixture model. The new algorithm defines a non-similarity measurement for the class attribute of pixels using the Gaussian mixture model. This algorithm has the ability to perform high precision fitting of data with a statistical distribution, thereby effectively eliminating the influence of noise on the segmentation result. Moreover, fuzzy region competition is introduced to define the prior probability of the neighborhood that will be regarded as the weight of the Gaussian component. This process enhances the robustness of large-scale image segmentation. Finally, we acquire the image data from Google Earth and conduct experiments; the experimental results show that this new method has the feasibility and effectiveness and can achieve highly accurate segmentation results compared with current state-of-the-art methods.