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2 December 2005 Vector data error analysis for remote sensing-based urban change mapping
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Proceedings Volume 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications; 60453C (2005)
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China
Nowadays, rapid urban growth will affect large social, environmental, economic and public health impacts in China. There is therefore a need for timely spatial information on urban change, i.e. urban change mapping. Aviation survey and satellite remote sensing are the main ways to obtain the information of earth surface. Compared to aviation survey, the satellite remote sensing makes it cost-efficient for urban change mapping at 1:10,000 scale. SPOT5 remotely sensed images are the more befitting data for this application than other satellite remotely sensed data now. The key technical problem is that whether the vector data error of urban change is within the mapping error limitation at this scale. In order to obtain the vector data precision and provide the precision reference for the application of SPOT5 image to urban change mapping, a case study was taken in Yangxunqiao of Shaoxing city. Based on the SPOT5 images acquired and the ground control points (GCPs) and check points taken by differential GPS through field survey, the geometric correction of images and urban change mapping at 1:10,000 scale were performed. The relevant indices were used to evaluate point position error, line feature error and polygon feature error of urban change vector data with field surveying data and simultaneous IKONOS images. The point position precision results were: root mean square (RMS) of X-3.93 m, RMS of Y-4.13 m, RMS of plane -5.71 m and the average relative polygon area accuracy was 88.05%. Finally the conclusion was made that urban change mapping based on SPOT5 image can satisfy well with the precision demand of 1:10,000 scale.
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Xiuli Feng, Ke Wang, Liming Lou, and Bin Zhou "Vector data error analysis for remote sensing-based urban change mapping", Proc. SPIE 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications, 60453C (2 December 2005);

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