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《Science & Technology Review》 2009-21
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An Object-Oriented Optimal Scale Choice Method for High Spatial Resolution Remote Sensing Image

ZHANG Jun1, WANG Yunjia2, LI Yan2, WANG Xingfeng2 1. No.3 Topographic Survey Team, State Bureau of Surveying and Mapping, Harbin 150081, China 2. School of Environment & Spatial Informatics, China University of Mining and Technology, Xuzhou 221008, Jiangsu Province, China  
The traditional pixel-based information extraction and classification method is not suitable for processing high spatial resolution remote sensing images because it only focuses on spectral information and ignores information concerning texture, shape and structure related with adjacent pixels. The object-oriented information and classification extraction approach can solve this problem, with the basic unit being the image object, which enjoys good integrity and uniqueness in a multi-scale segmentation algorithm. The related experimental researches show that it is necessary to extract the region of interest in optimal scale images. In view of this, the RMAS method is based on analysis of the limitations of two optimal scale selecting methods, according to the best classification principle as "homogeneity in class, heterogeneity between classes". This method makes the heterogeneity in class the minimum and that between class the maximum when RMAS is the maximum, so the segmentation scale is optimal. According to the principle of the highest information extraction accuracy based on the optimal scale, the experiment has verified the feasibility of this method and the classification results are found to be better. It is shown that several local peaks appear in the curve of RMAS and that the optimal scale is relative and usually in a range of values. So, it is difficult to extract information using only one scale for the class with a large area. The optimal scale should be very carefully chosen according to specific application cases.
【Fund】: 西部测图项目;; 国家自然科学基金重点项目(50534050);; 矿山空间信息技术国家测绘局重点实验室开放基金项目(KLM200819)
【CateGory Index】: TP751
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Chinese Journal Full-text Database 1 Hits
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Chinese Journal Full-text Database 10 Hits
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