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

Cunjin Xue,Yilong Li,Qing Dong,Xiaohong Li,Shuchao Wu,Xing Fan

doi:10.3390/rs70709149

Cunjin Xue, Yilong Li + Show 4 more

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https://doi.org/10.3390/rs70709149

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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.

Highlights

Series of images taken by remote sensing over long periods of time constitute the main source of continuous and consistent information about the marine environment, and offer opportunities for monitoring its variations and for understanding the associated relationships among parameters at large scale [1,2]
Given that object- and pixel-based mining strategies are complementary components and that object-based strategies are discussed in our previous work [24], the current case study adopts pixel-based mining strategies to explore marine spatiotemporal association patterns
The information threshold is set to the mean value to obtain pair-wise related items, the time interval is set to zero, and the support, confidence, and lift thresholds are set to 10%, 60%, and 2.0%, respectively

Summary

Introduction

Series of images taken by remote sensing over long periods of time constitute the main source of continuous and consistent information about the marine environment, and offer opportunities for monitoring its variations and for understanding the associated relationships among parameters at large scale [1,2]. El Niño–Southern Oscillation (ENSO) make up a complex and interrelated system [3,4,5] The complexities of such a system require analysis techniques that go beyond the conventional methods of spatiotemporal analysis, such as empirical orthogonal functions [6], canonical analysis [7], and singular value decomposition [8]. Such analyses require an inductive mining technique that accounts for the complex set of interdependencies [9,10,11]. It has been used to develop techniques such as spatiotemporal mining frameworks [12,13,14,15,16], mining algorithms [17,18,19], and knowledge visualization techniques [13,14,20]

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