Multi-Agent Recognition System based on Object Based Image Analysis Using WorldView-2

Abstract

In this paper, using spatial and spectral characteristics of the WorldView-2 satellite imagery, capabilities of multi-agent systems are used for solving multiple object recognition difficulties in complex urban areas. The methodology has two main steps: object based image analysis (OBIA) and multiagent object recognition. In the first step, segmentation and multi-process object classification based on spectral, textural, and structural features are performed. Classified regions are used as an input dataset in the multi-agent system in order to modify object recognition results. According to the results from the object based image analysis process, using contextual relations and structural features, the overall accuracy and Kappa improved by 17.79 percent and 0.253, respectively. Using knowledge-based reasoning and cooperative capabilities of agents in the multi-agent system in this paper, most of the remaining difficulties are decreased and values 90.95 percent and 0.876 are obtained for the overall accuracy and Kappa, respectively, of the object recognition results.