Abstract
Dynamically changing urban areas require periodic automatic monitoring, but urban areas include various objects and different objects show diverse appearances. This makes it difficult to effectively detect urban areas. A region-growing method using the Markov random field (MRF) model is proposed for urban detection. It consists of three modules. First, it provides an automatic urban seed objects extraction approach by designing three features with respect to urban characteristics. Second, the method uses an object-based MRF to model the spatial relationship between urban seed objects and surrounding objects. Third, a MRF-based region-growing criterion is proposed to detect urban areas based on seed points and spatial constraints. The strength of the proposed method lies in two aspects. One is that automatic selection of seed points is presented instead of manual selection. The other one is that the region-growing technique, instead of probabilistic inference, is used to solve the MRF optimization problem. Experiments on aerial images and SPOT5 images demonstrate that our method provides a better performance compared with the region-growing method, the classical and object-based MRF methods, or some other state-of-art methods.
Highlights
In recent years, urban detection has become more and more crucial for many applications
Our method provides an unsupervised way to detect urban areas, which makes it possible to capture the correlations among various urban objects by combining the benefits of region growing and the Markov random field (MRF) model
We proposed an unsupervised urban detection method by unifying the regiongrowing method and the MRF model
Summary
Urban detection has become more and more crucial for many applications. Considering the conflict between the need for periodically detecting urban areas and the high-human cost, many approaches had been proposed to automatically detect urban areas from remote sensing images.[1,2,3,4,5,6,7,8] an urban area is an abstract semantic object It is a comprehensive region including several subobjects such as buildings, roads, trees, water bodies, grass spaces, etc. Using the probabilistic inference of the MRF model in terms of common probability distributions cannot appropriately detect urban areas Motivated by this observation, this paper proposes an MRF-based region-growing method to extract urban areas.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have