Bayesian and Geostatistical Approaches to Combining Categorical Data Derived from Visual and Digital Processing of Remotely Sensed Images

编号 zgly0000375110

文献类型 期刊论文

文献题名 Bayesian and Geostatistical Approaches to Combining Categorical Data Derived from Visual and Digital Processing of Remotely Sensed Images

作者 ZHANGJingxiong  LIDeren 

作者单位 professor 

母体文献 Geo-spatial Information Science;地球空间信息科学学报: 英文版 

年卷期 2005,8(2)

页码 90-97,137

年份 2005 

分类号 TP751.1 

关键词 贝叶斯地理统计  遥感图像  计算机仿真  数据处理 

文摘内容 This paper seeks a synthesis of Bayesian and geostatistical approaches to combining categorical data in the context of remote sensing classification. By experiment with aerial photographs and Landsat TM data, accuracy of spectral, spatial, and combined classification results was evaluated. It was confirmed that the incorporation of spatial information in spectral classification increases accuracy significantly. Secondly, through test with a 5-class and a 3-class classification schemes, it was revealed that setting a proper semantic framework for classification is fundamental to any endeavors of categorical mapping and the most important factor affecting accuracy. Lastly, this paper promotes non-parametric methods for both definition of class membership profiling based on band-specific histograms of image intensities and derivation of spatial probability via indicator kriging, a non-parametric geostatistical technique。