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《Remote Sensing Information》 2006-05
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Classification of High-resolution Remote Sensing Images Using Object-oriented Method

CAO Xue,KE Chang-qing (Department of Urban and Resources Sciences, Nanjing University 210093 China)  
High-resolution remote sensing images have many more spatial characteristics than low-resolution data except spectral characteristics. Object-oriented image classification is a new technique in this research field, it can make most use of their advantages to extract information compared to the conventional pixel-oriented methods.In this case study, we classified QUICKBIRD image of Shenzhen city with the new method. Firstly, the image was multi-scale segmented by Fractal Net Evolution Approach to get objects; and then, we selected some characteristic parameters for realization according to spectral and spatial features of image objects. These different objects could be recognized easily using some suitable characteristics; finally, multiple level classification was realized based on semantic structure in the study area. The result showed that classification accuracy was improved by using object-oriented method, and this approach provided a new way for classification of high-resolution remote sensing data.
【Fund】: 国家自然科学基金(40301013)项目资助
【CateGory Index】: TP75
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