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Journal of Applied Remote Sensing • Open Access

Raster-based visualization of abnormal association patterns in marine environments
Author(s): Lianwei Li; Cunjin Xue; Jian Liu; Zhenjie Wang; Lijuan Qin

Paper Abstract

The visualization in a single view of abnormal association patterns obtained from mining lengthy marine raster datasets presents a great challenge for traditional visualization techniques. On the basis of the representation model of marine abnormal association patterns, an interactive visualization framework is designed with three complementary components: three-dimensional pie charts, two-dimensional variation maps, and triple-layer mosaics; the details of their implementation steps are given. The combination of the three components allows users to request visualization of the association patterns from global to detailed scales. The three-dimensional pie chart component visualizes the locations where more marine environmental parameters are interrelated and shows the parameters that are involved. The two-dimensional variation map component gives the spatial distribution of interactions between each marine environmental parameter and other parameters. The triple-layer mosaics component displays the detailed association patterns at locations specified by the users. Finally, the effectiveness and the efficiency of the proposed visualization framework are demonstrated using a prototype system with three visualization interfaces based on ArcEngine 10.0, and the abnormal association patterns among marine environmental parameters in the Pacific Ocean are visualized.

Paper Details

Date Published: 11 June 2014
PDF: 13 pages
J. Appl. Remote Sens. 8(1) 083615 doi: 10.1117/1.JRS.8.083615
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Lianwei Li, East China Univ. of Petroleum (China)
Cunjin Xue, Institute of Remote Sensing and Digital Earth (China)
Jian Liu, Institute of Remote Sensing and Digital Earth (China)
Zhenjie Wang, East China Univ. of Petroleum (China)
Lijuan Qin, Institute of Remote Sensing and Digital Earth (China)


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