Multi-object spatial relationship model for high spatial resolution scene classification

Siqi Wu, Bei Zhao, Yanfei Zhong, Liangpei Zhang

Abstract: Most of the existed scene classification methods classify the scenes ignoring the semantic ground objects for high spatial resolution imagery. Though lots of methods are proposed to recognize the objects, there are lack of methods modeling the spatial relationship between objects for scene classification. Besides, the frequency vector of the semantic objects in the scenes is inadequate to model the spatial relationship. Therefore, to acquire the semantic relationship among multiple objects or between the objects and the scene, this paper developed a multi-object force histogram (MOFH) to model the topology of multiple objects, and proposed a multi-object spatial relationship model (MOSRM) by combining the frequency vector of the ground objects and MOFH for the high spatial resolution scene classification. The experiments show that the proposed method can outperform the scene classification based on the frequency vector of the semantic objects.