从地面所接收到的太阳直接辐射、天空散射辐射和临近地形反射附加的辐射三个方面分析计算地面每个像元的太阳总辐射,并在此基础上建立地表真实反射率恢复模型,实现对地形的辐射校正。在算法实现上,采用交互式数据语言(Interactive Data ...从地面所接收到的太阳直接辐射、天空散射辐射和临近地形反射附加的辐射三个方面分析计算地面每个像元的太阳总辐射,并在此基础上建立地表真实反射率恢复模型,实现对地形的辐射校正。在算法实现上,采用交互式数据语言(Interactive Data Language,IDL),结合6S大气校正模型和数字高程模型(DEM)进行编程实现。利用北京山区的TM遥感影像所做的实验表明该方法能有效地消除卫星影像中地形的影响,为影像的后续处理提供更真实的信息。展开更多
Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed imagery, and descriptors such as percent correctly classified pixels (PCC) and Kapp...Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed imagery, and descriptors such as percent correctly classified pixels (PCC) and Kappa coefficients of agreement have been devised as thematic accuracy metrics. However, such spatially averaged measures about accuracy neither offer hints about spatial variation in misclassification, nor are useful for quantifying error margins in derivatives, such as the areal extents of different land cover types and the land cover change statistics. Such limitations originate from the deficiency that spatial dependency is not accommodated in the conventional methods for error analysis. Geostatistics provides a good framework for uncertainty characterization in land cover information. Methods for predicting and propagating misclassification will be described on the basis of indicator samples and covariates, such as spectrally derived posteriori probabilities. An experiment using simulated datasets was carried out to quantify the error in land cover change derived from postclassification comparison. It was found that significant biases result from applying joint probability rules assuming temporal independence between misclassifications across time, thus emphasizing the need for the stochastic simulation in error modeling. Further investigations, incorporating indicators and probabilistic data for mapping and propagating misclassification, are anticipated.展开更多
文摘从地面所接收到的太阳直接辐射、天空散射辐射和临近地形反射附加的辐射三个方面分析计算地面每个像元的太阳总辐射,并在此基础上建立地表真实反射率恢复模型,实现对地形的辐射校正。在算法实现上,采用交互式数据语言(Interactive Data Language,IDL),结合6S大气校正模型和数字高程模型(DEM)进行编程实现。利用北京山区的TM遥感影像所做的实验表明该方法能有效地消除卫星影像中地形的影响,为影像的后续处理提供更真实的信息。
基金Supported by the National 973 Program of China (No. 2006CB701302)the Hubei Department of Science and Technology (No. 2007ABA276)
文摘Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed imagery, and descriptors such as percent correctly classified pixels (PCC) and Kappa coefficients of agreement have been devised as thematic accuracy metrics. However, such spatially averaged measures about accuracy neither offer hints about spatial variation in misclassification, nor are useful for quantifying error margins in derivatives, such as the areal extents of different land cover types and the land cover change statistics. Such limitations originate from the deficiency that spatial dependency is not accommodated in the conventional methods for error analysis. Geostatistics provides a good framework for uncertainty characterization in land cover information. Methods for predicting and propagating misclassification will be described on the basis of indicator samples and covariates, such as spectrally derived posteriori probabilities. An experiment using simulated datasets was carried out to quantify the error in land cover change derived from postclassification comparison. It was found that significant biases result from applying joint probability rules assuming temporal independence between misclassifications across time, thus emphasizing the need for the stochastic simulation in error modeling. Further investigations, incorporating indicators and probabilistic data for mapping and propagating misclassification, are anticipated.
