at 11:26 on December 22,a LM-4B launch vehicle lifted the ZY-1 02C satellite into space from the Taiyuan Satellite Launch Center,marking the complete success of the final launch mission of this year.13 minutes later,t...at 11:26 on December 22,a LM-4B launch vehicle lifted the ZY-1 02C satellite into space from the Taiyuan Satellite Launch Center,marking the complete success of the final launch mission of this year.13 minutes later,the satellite entered into sun-synchronous circular orbit after separating with the rocket.展开更多
资源一号(ZY-1)02C卫星作为我国高分辨率遥感数据卫星之一,正广泛应用于各个领域。该卫星数据采用国际上广泛使用的有理函数模型(Rational Function Model,RFM),并提供有理函数多项式系数(Rational Polynomial Coefficients,RPC)。针对Z...资源一号(ZY-1)02C卫星作为我国高分辨率遥感数据卫星之一,正广泛应用于各个领域。该卫星数据采用国际上广泛使用的有理函数模型(Rational Function Model,RFM),并提供有理函数多项式系数(Rational Polynomial Coefficients,RPC)。针对ZY-1 02C星1级辐射校正数据格式特点,在充分了解数据文件结构基础上,使用IDL语言进行开发,通过RFM反演其RPC,实现HR影像的几何校正等预处理。实验结果表明,程序执行快速稳定,精度可靠,影像镶嵌效果好,可以有效使用RFM实现大数据量影像的系统几何校正,避免重复操作中间数据的繁琐步骤,提高工作效率,具有实用价值。展开更多
The Chinese ZY-1 02C satellite is one of the most advanced high-resolution earth observation systems designed for terrestrial resource monitoring. Its capability for comprehensive landscape classification, especially ...The Chinese ZY-1 02C satellite is one of the most advanced high-resolution earth observation systems designed for terrestrial resource monitoring. Its capability for comprehensive landscape classification, especially in urban areas, has been under constant study. In view of the limited spectral resolution of the ZY-1 02C satellite （three bands）, and the complexity and hetero- geneity across urban environments, we attempt to test its performance of urban landscape classification by combining a multi- variable model with an object-oriented approach. The multiple variables including spectral reflection, texture, spatial autocorre- lation, impervious surface fraction, vegetation, and geometry indexes were first calculated and selected using forward stepwise linear discriminant analysis and applied in the following object-oriented classification process. Comprehensive accuracy as- sessment which adopts traditional error matrices with stratified random samples and polygon area consistency （PAC） indexes was then conducted to examine the real area agreement between a classified polygon and its references. Results indicated an overall classification accuracy of 92.63% and a kappa statistic of 0.9124. Furthermore, the proposed PAC index showed that more than 82% of all polygons were correctly classified. Misclassification occurred mostly between residential area and barren/farmland. The presented method and the Chinese ZY-1 02C satellite imagery are robust and effective for urban landscape classification.展开更多
文摘at 11:26 on December 22,a LM-4B launch vehicle lifted the ZY-1 02C satellite into space from the Taiyuan Satellite Launch Center,marking the complete success of the final launch mission of this year.13 minutes later,the satellite entered into sun-synchronous circular orbit after separating with the rocket.
基金supported by the Chinese Ministry of Environmental Protection(No.STSN-05-11)Zhejiang Key Scientific and Technological Innovation Team Projects(No.2010R50030)the National Natural Science Foundation of China(No.31172023)
文摘The Chinese ZY-1 02C satellite is one of the most advanced high-resolution earth observation systems designed for terrestrial resource monitoring. Its capability for comprehensive landscape classification, especially in urban areas, has been under constant study. In view of the limited spectral resolution of the ZY-1 02C satellite （three bands）, and the complexity and hetero- geneity across urban environments, we attempt to test its performance of urban landscape classification by combining a multi- variable model with an object-oriented approach. The multiple variables including spectral reflection, texture, spatial autocorre- lation, impervious surface fraction, vegetation, and geometry indexes were first calculated and selected using forward stepwise linear discriminant analysis and applied in the following object-oriented classification process. Comprehensive accuracy as- sessment which adopts traditional error matrices with stratified random samples and polygon area consistency （PAC） indexes was then conducted to examine the real area agreement between a classified polygon and its references. Results indicated an overall classification accuracy of 92.63% and a kappa statistic of 0.9124. Furthermore, the proposed PAC index showed that more than 82% of all polygons were correctly classified. Misclassification occurred mostly between residential area and barren/farmland. The presented method and the Chinese ZY-1 02C satellite imagery are robust and effective for urban landscape classification.
