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

Xue, Chao; Qian, Dong; Li, Xiaohong; Fan, Xingwang; Li, Yilong; Wu, Shiyou

doi:10.3390/rs70709149

Cited by 3 publications

(2 citation statements)

References 35 publications

(65 reference statements)

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“…The main function of RemoteAgri consisted of time series of extraction and time series of pattern exploration. Xue et al developed the image-driven remote-sensing mining system RSMapMining to explore marine knowledge from remote sensing images, which integrated an image preprocessing module, a pattern mining module and a knowledge visualization module [53].…”

Section: Introductionmentioning

confidence: 99%

PoSDMS: A Mining System for Oceanic Dynamics with Time Series of Raster-Formatted Datasets

Xue

et al. 2022

Remote Sensing

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Many effective and advanced methods have been developed to explore oceanic dynamics using time series of raster-formatted datasets; however, they have generally been designed at a scale suitable for data observation and used independently of each other, despite the potential advantages of combining different modules into an integrated system at a scale suited for dynamic evolution. From raster-formatted datasets to marine knowledge, we developed and integrated several mining algorithms at a dynamic evolutionary scale and combined them into six modules: a module of raster-formatted dataset pretreatment; a module of process-oriented object extraction; a module of process-oriented representation and management (process-oriented graph database); a module of process-oriented clustering; a module of process-oriented association rule mining; and a module of process-oriented visualization. On the basis of such modules, we developed a process-oriented spatiotemporal dynamic mining system named PoSDMS (Process-oriented Spatiotemporal Dynamics Mining System). PoSDMS was designed to have the capacity to deal with at least six environments of marine anomalies with 40 years of raster-formatted datasets, including their extraction, representation, storage, clustering, association and visualization. The effectiveness of the integrated system was evaluated in a case study of sea surface temperature datasets during the period from January 1982 to December 2021 in global oceans. The main contribution of this study was the development of a mining system at a scale suited for dynamic evolution, providing an analyzing platform or tool to deal with time series of raster-formatted datasets to aid in obtaining marine knowledge.

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Section: Introductionmentioning

confidence: 99%

PoSDMS: A Mining System for Oceanic Dynamics with Time Series of Raster-Formatted Datasets

Xue

et al. 2022

Remote Sensing

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“…Long-term remote sensing images constitute the main source of continuous and consistent information about Earth’s land and oceans and offer new opportunities to improve our understanding of these marine spatial patterns on a large scale [ 4 , 5 ]. As an inductive method, spatiotemporal data mining shows more promise for discovering spatial patterns among multiple geographic parameters than the traditional statistical analysis [ 6 – 8 ], especially with the remote sensing images in recent decades [ 3 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Tree-based approach for exploring marine spatial patterns with raster datasets

Liu¹,

Xue²,

Su³

2017

PLoS ONE

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From multiple raster datasets to spatial association patterns, the data-mining technique is divided into three subtasks, i.e., raster dataset pretreatment, mining algorithm design, and spatial pattern exploration from the mining results. Comparison with the former two subtasks reveals that the latter remains unresolved. Confronted with the interrelated marine environmental parameters, we propose a Tree-based Approach for eXploring Marine Spatial Patterns with multiple raster datasets called TAXMarSP, which includes two models. One is the Tree-based Cascading Organization Model (TCOM), and the other is the Spatial Neighborhood-based CAlculation Model (SNCAM). TCOM designs the “Spatial node→Pattern node” from top to bottom layers to store the table-formatted frequent patterns. Together with TCOM, SNCAM considers the spatial neighborhood contributions to calculate the pattern-matching degree between the specified marine parameters and the table-formatted frequent patterns and then explores the marine spatial patterns. Using the prevalent quantification Apriori algorithm and a real remote sensing dataset from January 1998 to December 2014, a successful application of TAXMarSP to marine spatial patterns in the Pacific Ocean is described, and the obtained marine spatial patterns present not only the well-known but also new patterns to Earth scientists.

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Multi-scale regionalization based mining of spatio-temporal teleconnection patterns between anomalous sea and land climate events

Shi

Deng

et al. 2017

J. Cent. South Univ.

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

Cited by 3 publications

References 35 publications

PoSDMS: A Mining System for Oceanic Dynamics with Time Series of Raster-Formatted Datasets

PoSDMS: A Mining System for Oceanic Dynamics with Time Series of Raster-Formatted Datasets

Tree-based approach for exploring marine spatial patterns with raster datasets

Multi-scale regionalization based mining of spatio-temporal teleconnection patterns between anomalous sea and land climate events

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