On the contrary, some subobjects that consist of a typical urban pattern can be well detected according to their own characteristics. For instance, man-made objects, such as buildings6,7,16 and roads,17–19 usually have compact shapes. In contrast, spectral features are important for detecting natural objects, e.g., vegetations20,21 and water bodies.21 Hence, an alternative way of urban detection is to first detect some urban subobjects and then extract the entire urban area based on the extracted subobjects. The region-based classification is a widely used approach to detect certain land cover objects.22–25 However, different urban areas may consist of different subobjects. Meanwhile, some subobjects, such as trees and water bodies, may appear in both urban areas and the nonurban areas. This phenomenon makes the region-based urban detection methods challenging, even though each urban subobject can be accurately classified. As urban objects are spatially adjacent, one possible way to answer this problem is to take the spatial information of objects into account. The Markov random field (MRF)26 model provides a statistical way to model spatial contextual information, and it has been extended to the region level for image classification. 23–25 For example, Wu et al.23 used some rectangular regions as the initial objects and then classified the polarimetric SAR images using the Wishart MRF. However, the accuracy of classification is still limited when the rectangular region is located on the edge of some objects. Zhang et al.24 improved this method by using a mean shift to obtain the finer initial regions. Wang and Zhang25 used the Gaussian distribution to recognize images instead of the Wishart distribution. Although these MRF-based classification approaches usually obtained remarkable results, they assumed that each land class obeyed a certain probability distribution, e.g., the Wishart or Gaussian distribution. Nevertheless, the assumption about the probability distribution does not hold in the case of detecting urban areas, as urban areas are often represented as complex regions with various subobjects. Using the probabilistic inference of the MRF model in terms of common probability distributions cannot appropriately detect urban areas.