A spatiotemporal mining framework for abnormal association patterns in marine environments with a time series of remote sensing images |
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| Institution: | 1. Key Laboratory of Western China''s Environmental Systems (Ministry of Education), College of Earth and Environment Sciences, Lanzhou University, Lanzhou, Gansu 73000, China;2. State Key laboratory of Frozen Soil Engineering, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences (CAS), Lanzhou 730000, China;1. School of Mathematics, Liaoning University, Shenyang 110036, PR China;2. Huanghe Science & Technology Colloege, Zhengzhou 450006, PR China;3. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, PR China;4. School of Equipment Engineering, Shenyang Ligong University, Shenyang 110159, PR China |
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| Abstract: | A spatiotemporal mining framework is a novel tool for the analysis of marine association patterns using multiple remote sensing images. From data pretreatment, to algorithm design, to association rule mining and pattern visualization, this paper outlines a spatiotemporal mining framework for abnormal association patterns in marine environments, including pixel-based and object-based mining models. Within this framework, some key issues are also addressed. In the data pretreatment phase, we propose an algorithm for extracting abnormal objects or pixels over marine surfaces, and construct a mining transaction table with object-based and pixel-based strategies. In the mining algorithm phase, a recursion method to construct a direct association pattern tree is addressed with an asymmetric mutual information table, and a recursive mining algorithm to find frequent items. In the knowledge visualization phase, a “Dimension–Attributes” visualization framework is used to display spatiotemporal association patterns. Finally, spatiotemporal association patterns for marine environmental parameters in the Pacific Ocean are identified, and the results prove the effectiveness and the efficiency of the proposed mining framework. |
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| Keywords: | Marine association pattern Spatiotemporal mining framework Global change Remote sensing Pacific Ocean |
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