空间数据融合的研究进展:从经典方法到扩展方法

随着数据获取方式和技术的多样化，多源空间数据持续累积，迫切需要研究数据集成方法，以更好地为地学研究与应用提供信息和技术支持。将各种用于集成多源空间数据的处理过程统称为数据融合，并从经典方法和扩展方法的角度，分别综述相关研究进展。为综述经典方法的研究进展，依据空间对象数据模型和场数据模型，理清了数据融合及其相关数据处理较成熟的方法；对于扩展方法，阐述了多点地统计方法、统计-机理型方法、多尺度分析与重构方法、信息论方法等。为更有效地支持多源异构数据环境下的数据融合，讨论了尺度不匹配、语义不一致性、时间维等问题。

Progress in Spatial Data Fusion:From Classic Approaches to Extended Methods

With diversification of data acquisitions and associated technologies, there is steady accumulation of multi-source spatial data, prompting more research on effective and fast data integration to provide information and technical support for geospatial research and applications. In this paper, procedures for integrating multi-source data are referred to as data fusion. Research developments are reviewed by considering them as classic and extended types of methods, respectively. To review the progress in classic methods for data fusion and related data processing, this paper clarifies some of the relatively well-established methods using a typology of object-and field-based models of spatial data. For extended methods, the paper discusses multi-point geostatistics, statistical-mechanistic methods, multi-scale signal analysis and reconstruction, and information-theoretic strategies. Some of the issues, such as scale mismatch, semantic inconsistency, and the temporal dimension, are discussed in the hope of better supporting the fusion of multi-sourced and heterogeneous data. Further research will enhance theoretical foundations of geographic information to enrich methodologies for spatial data and their analyses, and help add to the applicability and value of spatial information.