多源土地覆被遥感信息融合及数据重构研究
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摘要
针对当前土地覆被遥感数据存在的问题及现有融合算法的不足提出了基于Dempster-Shafer理论和贝叶斯最大熵（Bayesian Maximum Entropy，BME）理论的多源土地覆被遥感数据融合及重构框架。研究首先评价了四种土地覆被遥感数据在国家及区域尺度的类别精度及混淆特征，构建了多源数据的空间一致性及类别均质性图谱。研究提出了基于LCCS分类体系和最小距离向量计算土地覆被遥感数据及MODIS LAI针对目标分类体系的基本概率函数的计算方法，实现了Dempster-Shafer理论框架下多源土地覆被遥感数据及辅助信息融合，并从多角度对融合结果进行了评价。针对典型研究区域，对比分析了指示克里金与贝叶斯最大熵模型在宏观及微观尺度的数据重构结果，挖掘了预测位置邻域内已知点个数对于预测精度及预测时间的定量影响，分析了在软数据精度较低情况下贝叶斯最大熵模型联合使用硬数据及软数据相对于单独使用硬数据进行数据重构的结果。
In view of the problems of the existing global land cover products and the deficiency ofcurrent data fusion methods, this study aims to develop a general framework for building a hybridland cover map by the synergistic combination of a number of land-cover classifications withdifferent legends and spatial resolutions based on Dempster-Shafer and Bayesian MaximumEntropy theory. This paper first evaluated the category accuracy and confusion characteristic offour kinds of land cover products on national and regional scales, then constructed the spatialagreement and spatial homogeneity map and their quantitative relationships with categoryaccuracy. The computational methods of the land cover products and MODIS LAI productscorrespond to the target land cover legend was derived through the LCCS and the minimumdistance vector space; and a Multi-source integrated Land Cover map was generated based on theDempster–Shafer evidence theory and the map’s precision was evaluated from different aspects.Then we compared and analyzed the data restructure result on both macro and micro scalebetween Indicator Kriging and Bayesian Maximum Entropy. We also evaluated the influence ofthe number of data locations in the neighborhood on the predicted accuracy and consume time,and assessed the scale of information yielded by the adjunction of an additional data location inthe prediction process. At last, we compared the results of data restructure between the way ofusing the hard data and soft data together and using the hard data alone,when the accuracy of thesoft data is lower.
In view of the problems of the existing global land cover products and the deficiency ofcurrent data fusion methods, this study aims to develop a general framework for building a hybridland cover map by the synergistic combination of a number of land-cover classifications withdifferent legends and spatial resolutions based on Dempster-Shafer and Bayesian MaximumEntropy theory. This paper first evaluated the category accuracy and confusion characteristic offour kinds of land cover products on national and regional scales, then constructed the spatialagreement and spatial homogeneity map and their quantitative relationships with categoryaccuracy. The computational methods of the land cover products and MODIS LAI productscorrespond to the target land cover legend was derived through the LCCS and the minimumdistance vector space; and a Multi-source integrated Land Cover map was generated based on theDempster–Shafer evidence theory and the map’s precision was evaluated from different aspects.Then we compared and analyzed the data restructure result on both macro and micro scalebetween Indicator Kriging and Bayesian Maximum Entropy. We also evaluated the influence ofthe number of data locations in the neighborhood on the predicted accuracy and consume time,and assessed the scale of information yielded by the adjunction of an additional data location inthe prediction process. At last, we compared the results of data restructure between the way ofusing the hard data and soft data together and using the hard data alone,when the accuracy of thesoft data is lower.
引文
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