Object Recognition in Remote Sensing Images Based on Knowledge Representation and Inference
In order to realize object recognition in remote sensing images,basing on knowledge representation and inference,an object recognition method is proposed in this paper;In proposed method,image segmentation is performed firstly,and each segmented region is characterized by means of a set of features,which are related to spectral,spatial and contextual attributes,are extracted from each segmented regionin in order to perform object recognition;A domain is then modeled in a knowledge-formalized ontology that defines a set of concepts(such as buildings,vegetation,roads,water,etc.),their characteristics and their relationships to each other.In order to allocate a semantic meaning to each region,a feature-oriented matching between an object and the concepts of ontology is proposed,and the original matching measure of the ontology computing and the traversal process based on heuristic are given to realize the recognition of the whole image;The experimental results based on Super-View-1 satellite image show that the proposed method is not only efficient and robust,but also superior to other existing image recognition methods in terms of the average values of precision,recall rate and F-measure.
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