基于灾害信息的洪水融合三维场景适宜性可视化方法

IF 1.8 Q3 REMOTE SENSING

International Journal of Image and Data Fusion Pub Date : 2021-08-06 DOI:10.1080/19479832.2021.1961315

LanXiang Luo, Jun Zhu, Lin Fu, S. Pirasteh, Weilian Li, Xiao Han, Yukun Guo

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引用次数: 3

摘要

摘要增强洪水的可视化对于用户了解灾害信息至关重要。然而，现有的洪水可视化方法存在场景内容表达匮乏、难以快速获取灾害信息、缺乏对灾害场景的语义描述等不足。本研究提出了以灾害信息为指导的洪水灾害场景建模的约束规则，以确定灾害的内容和相关性。我们创建了一个融合了基础地理场景、洪水时空过程和灾害对象模型的灾害融合表达模型，以获得完整的洪水灾害场景。最后，我们提出了一种洪水场景的动态适宜性可视化方法，以提高灾害信息的可读性。我们将所提出的模型应用于中国四川省丹巴县洪水，以验证该模型的性能有效性。该发现显示了特定时间不同监测点的流速和洪水深度的变化范围，也显示了研究区域内灾害对象的灾害程度。结果表明，该方法可以有效地实现三维灾害场景的融合和洪水的动态适应性可视化，帮助用户快速了解洪水，获取有用的灾害信息。

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A suitability visualisation method for flood fusion 3D scene guided by disaster information

ABSTRACT Enhancing the visualisation of floods is essential for users to understand disaster information. However, existing flood visualisation methods have some deficiencies like scarce scene content expression and difficulty obtaining disaster information quickly and lacked a semantic description of the disaster scenes. This study presents constraint rules of flood disaster scene modelling guided by disaster information to determine the disaster’s content and correlation. We created a disaster fusion expression model to obtain the complete flood disaster scene which integrated basic geography scene, flood space-time process and disaster object models. Finally, we proposed a dynamic suitability visualisation method for the flood scene to increase the readability of disaster information. We applied the proposed model in the Danba County flood in Sichuan Province, China, to validate the model’s performance effectiveness. The finding shows the variation range of flow velocity and flood depth at different monitoring points at a specific time, and also shows the disaster level of disaster objects in the study area. It indicates that the proposed method can effectively realise the fusion of 3D disaster scenes and the dynamic suitability visualisation of floods and help users understand floods quickly and get useful disaster information.

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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.).