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Remote Sens. 2015, 7(7), 9149-9165; doi:10.3390/rs70709149

A Remote-Sensing-Driven System for Mining Marine Spatiotemporal Association Patterns

1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
Key Laboratory of the Earth Observation, Sanya 572029, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Janet Nichol and Prasad S. Thenkabail
Received: 26 March 2015 / Revised: 27 June 2015 / Accepted: 4 July 2015 / Published: 17 July 2015
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Abstract

Remote sensing is widely used to analyze marine environments. While many effective and advanced methods have been developed, they are generally used independently of each other, despite the potential advantages of combining different modules into an integrated system. We develop here an image-driven remote-sensing mining system, RSMapMining (Remote Sensing driven Marine spatiotemporal Association Pattern Mining system), which consists of three modules. The image preprocessing module integrates image processing techniques and marine extraction methods to build a mining database. The pattern mining module integrates popular algorithms to implement the mining process according to the mining strategies. The third module, knowledge visualization, designs a series of interactive interfaces to visualize the marine data at a variety of scales, from global to grid pixel. The effectiveness of the integrated system is tested in a case study of the northwestern Pacific Ocean. The main contribution of this study is the development of a mining system to deal with marine remote sensing images by integrating popular techniques and methods ranging from information extraction, through visualization, to knowledge discovery. View Full-Text
Keywords: marine remote sensing; image-driven; mining system; association pattern; northwestern Pacific Ocean marine remote sensing; image-driven; mining system; association pattern; northwestern Pacific Ocean
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Xue, C.; Dong, Q.; Li, X.; Fan, X.; Li, Y.; Wu, S. A Remote-Sensing-Driven System for Mining Marine Spatiotemporal Association Patterns. Remote Sens. 2015, 7, 9149-9165.

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