自然实体的数字建模及其伪表示

IF 1.8 Q3 REMOTE SENSING

International Journal of Image and Data Fusion Pub Date : 2020-11-01 DOI:10.1080/19479832.2020.1838630

Cifeng Wang, Ziming Zou, Xiaoyan Hu, Yunlong Li, Xi Bai

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引用次数: 0

摘要

随着新方法的发展和传感器技术的巨大进步，对传感器的观察和研究越来越立体、全面。为了构建支撑过程演化预测、结构发现和关联分析等计算的多源数据融合系统，需要对自然实体进行数字建模，从而构建相应的数字实体。本文介绍了地球科学中的概念和模型，并讨论了数字建模理论中被忽视的问题。在此基础上，建立了统一的概念模型及其伪表示(BPRModel)。此外，还说明了该模型在特定自然实体(即地球磁层)研究中的应用。

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Towards the digital modelling of natural entities and its Pseudo-representation

ABSTRACT With the development of new methods and the tremendous progress in transducer technology, the observations and researches have become more and more stereoscopic and full-scale. In order to build the multi-source data fusion system propping up the computations, such as process evolution prediction, structure discovery and association analysis, the digital modelling of the natural entity needs to be carried out, which would help build the corresponding digital entity. In this study, the concepts and models in geoscience are introduced, and the issues overlooked in the digital modelling theories are discussed. On this basis, a unified conceptual model and its pseudo-representation (BPRModel) are built. Furthermore, the application of the model is illustrated under the research of specific natural entity, that is to say, the Earth’s magnetosphere.

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

期刊介绍： International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).