Urban area detection from high-spatial resolution remote sensing imagery using Markov random field-based region growing

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.