Abstract: Based on the data sources of high-resolution remote sensing images and high-precision land cover classification products collected in the Geographic National Conditions Monitoring Project of China, a nationwide massive sample data acquisition method is proposed for intelligent classification of remote sensing images by using location matching technology. According to the characteristics analysis of data sources, the key technologies such as quantitative weight setting for each county, coordinate's projection conversion, raster grid's gray resampling, invalid sample data's filtering, land cover's classification code conversion, sample data's file identification, and specific types of land cover's sample data acquisition are researched. And a sample data pair formed by remote sensing image and classification label data is constructed. Besides, sample data automatic acquisition software is independent developed. By using this whole approach, the national scale massive sample data has been achieved, which is gathered by each unit of county level administrative division. The results of 5 different counties are selected to evaluate the practicability and operational performance of the method. The results show that this method can improve the calculation response speed of massive sample data produced once in whole country, and the collected sample data can meet the demand of high quality and large-scale sample sources for intelligent classification of remote sensing images, and improves the accuracy of classification and prediction of remote sensing images.

Key words: remote sensing, land cover, intelligent classification, sample, location matching, geographic national conditions monitoring