使用黄河三角洲海岸Landsat卫星遥感数据, 基于研究区域低分辨率6波段的海陆类型软分类结果及其变差函数, 以高分辨率8波段的指示变差函数为精细尺度先验信息模型, 采用数据探索性分析、协同指示克里格和序贯指示协同模拟技术, 生成海陆类型发生概率模拟图像, 通过等值线法提取海岸线空间分布特征。实验表明, 基于地统计学方法的超分辨率制图技术在低分辨率遥感数据中融合高分辨率空间结构先验模型, 可以较好表达精细尺度上的海岸线空间分布特征, 同时保持原始数据的海陆类型组分信息及其空间结构特征。地统计学方法集成多尺

The soft classification results of coarse-resolution data and the coarse & fine resolution variograms were derived, using the Landsat satellite data of the modern Yellow River Delta coast. Treating the fine-resolution indicator vriogram as the prior model of spatial structure of the study area, the super-resolution images of land & ocean class were generated using data exploratory analysis, indicator cokriging (ICK), and sequential indicator co-simulation (SIcS) techniques. Then the spatial distribution features of the coastline were extracted by the contouring method. Taken human interpretation output as the benchmark, the coastline mapping results derived from geostatistics showed better quality than that derived from traditional hard-classification methods. Super-resolution mapping techniques based on geostatistics can properly illustrate spatial distribution of coastline at fine scale; meanwhile maintain the class fraction values and the spatial structures of the original coarse-resolution data. By the form of super-resolution mapping of coastline, the potential of the geostatistical techniques in integrating multi-source and multi-scale spatial information has been demonstrated.