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

• Main Authors: Cunjin Xue, Qing Dong, Xiaohong Li, Xing Fan, Yilong Li, Shuchao Wu
• Format: Article
• Language: English
• Published: MDPI AG 2015-07-01
• Series: Remote Sensing
• Subjects: marine remote sensing; image-driven; mining system; association pattern; northwestern Pacific Ocean
• Online Access: http://www.mdpi.com/2072-4292/7/7/9149

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.