Research On Extracting Rural Residential Area From Very High Resolution Imagery Based On Ontological Modeling And Object Based Image Analysis

Rural residential area(RRA) was significant basic geographic information. Monitoring the time-spatial coordination of RRA fleetly and efficiently, not only played a momentous role in census of national geographic conditions, but also was of great positive significance to regional sustainable development. With the rapid development of remote sensing, it had many virtues, such as wide coverage, easily accessible and high spatial resolution. Therefore we could grasp the spatial information of RRA rapidly and accurately.At present, the research on information extraction of composite feature in high-resolution remote sensing images, mostly adopted the method to analysis structure-texture of RRA based on pixels, but the method had a lot of limitations. Therefore, exploring new ways to extract information of composite feature was inevitable trend. Under the background of big data on remote sensing, geographic information ontology modeling was an important content of study on automatic/semiautomatic land cover types identification from remote sensing image, and played an important role in information extraction of composite feature in high resolution remote sensing image. Gruninger etc.(1995) considered that the essence of ontology modeling was establishing logical model based on objects’ knowledge, such as concepts, relations, attributes, constraint conditions, etc.In this paper, using the WorldView Ⅱ satellite image of the Hetan Villiage and surrounding, in Shapotou District, Zhongwei County, Ningxia Province, China, It discussed the method about RRA information extraction based on ontology modeling and object-based image analysis, and focused on improving the integrity and accuracy. At first, it studied the way to build ontology model of RRA. Secondly, it discussed how to formal ontology model using OBIA. Finally, it used different ways to extract RRA information and verified that ontology modeling method was effective.The result indicated that,(1) putted forward the cognitive framework of RRA in high resolution imagery and built the RRA conceptual model to extract the information of rural residential area was a feasible idea.(2) RRA was a kind of complex feature. Its spectral information was very complex. Therefore, taking advantage of OBIA and ontology modeling was an effective way to extract complex feature information from high resolution remote sensing images. This method had positive significance.(3) At the time of building RRA ontology model, we should pay attention to the share knowledge used to construct ontology model.(4) Using an object-based image analysis method to formalize the conceptual model of rural residential, it could take advantage of the spectral characteristics, geometric feature, texture feature, spatial relationships and context information. So the RRA ontology model built before was more accurate. It improved accuracy and completeness of the information extraction.(5) Image segmentation quality serious affected the precision of result. More accurate segmentation led to more precision result. “PSE-NSR-ED2” was a better way to select the optimal segmentation result. It considered the both algebra difference and geometry difference.(6) Formalizing ontology model of RRA using OBIA included several steps: expressing the sets of concepts, relations, attributes and axioms with OBIA. They respectively represented “establish classification system”, “determine classification characteristics”, and “build rule sets”.