A multi-resolution texture-based visualization method for geographic vector fields by means of spatial variation features

doi:10.11947/j.AGCS.2020.20190160

Abstract

Abstract: How to visualize massive, high-dimensional, dynamic and multi-scale geospatial vector fields online under the network environment, has been the key issue for data integration and comprehensive analysis of massive and intensive vector fields. Geographic vector fields contain various irregular and multi-scale variation features and structures with prominent spatio-temporal heterogeneity. Due to global uniform sampling density, there are typical problems of occlusion and low feature discrimination in traditional texture-based vector field visualization. To overcome these shortcomings, this paper proposes a multi-resolution texture-based visualization method for geographic vector fields by means of spatial variation features. The proposed method uses composite information entropy as the description of spatial variation features, so as to drive the generation of feature-constrained multi-resolution noise, construction and enhancement of multi-resolution texture of vector fields in image space, which can effectively overcome the technical bottleneck of the traditional texture visualization method. Experimental results of the wind flow dataset prove the reliability and effectiveness of the method.

Key words: geospatial vector fields, multi-resolution, spatial variation features, information entropy, texture-based visualization

CLC Number:

P208

SHANG Daiyu, DING Yulin, ZHU Qing, WU Linbao. A multi-resolution texture-based visualization method for geographic vector fields by means of spatial variation features[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(5): 656-666.

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