A spatiotemporal mining framework for abnormal association patterns in marine environments with a time series of remote sensing images

Xue, Chao; Song, Wanjiao; Qin, Lijuan; Qian, Dong; Wen, Xin

doi:10.1016/j.jag.2014.12.009

Cited by 25 publications

(23 citation statements)

References 35 publications

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“…The levels −2 to +2 represent abnormally negative changes to abnormally positive changes. For the ENSO index the five levels represent strong La Niña, weak La Niña, a neutral condition, weak El Niño, and strong El Niño, using similar results to the general definition of El Niño and La Niña [16].…”

Section: Methods and Resultsmentioning

confidence: 68%

“…To deal with the co-location association patterns among parameters and the association patterns among regions, RSMapMining integrates complementary pixel-and object-based mining frameworks with multiple remote sensing images to find mining association patterns by grid pixels or by objects [16]. Regarding the evolution of marine association patterns, RSMapMining considers the evolution of an object from its start to its end as an event or process; it develops an event-or process-based mining model to explore the evolution of the association patterns.…”

Section: Spatiotemporal Association Pattern Mining Modulementioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

“…Bertolotto et al [13] and Compieta et al [14] designed a mining architecture that includes a data layer for data processing, an application layer for association acquisition, and a visualization layer for knowledge visualization. Xue et al [16] discussed pixel-and object-based spatiotemporal mining frameworks, and addressed some key issues ranging from image preprocessing, through information mining, to knowledge visualization.…”

Section: Introductionmentioning

confidence: 99%

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

Xue

Qian

et al. 2015

Remote Sensing

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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.

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Section: Methods and Resultsmentioning

confidence: 68%

Section: Spatiotemporal Association Pattern Mining Modulementioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Xue

Qian

et al. 2015

Remote Sensing

Self Cite

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“…Several approaches can do multi-scale analysis in either spatial or temporal domain (e.g., EMD, wavelet, and parallel adaptive kernel smoothing) [20][21][22]. Few methods can support for the spatio-temporal domain analysis in the spatio-temporal unified way [23,24]. With the separation of spatial and temporal signal in data analysis the spatio-temporal data can only be analyzed either spatially or temporally, which will lead to information missing, pattern inconsistency, and synchronizing difficulties.…”

Section: Introductionmentioning

confidence: 99%

Exploratory Method for Spatio-Temporal Feature Extraction and Clustering: An Integrated Multi-Scale Framework

Luo

Xiao

et al. 2015

IJGI

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This paper presents an integrated framework for exploratory multi-scale spatio-temporal feature extraction and clustering of spatio-temporal data. The framework combines the multi-scale spatio-temporal decomposition, feature identification, feature enhancing and clustering in a unified process. The original data are firstly reorganized as multi-signal time series, and then decomposed by the multi-signal wavelet. Exploratory data analysis methods, such as histograms, are used for feature identification and enhancing. The spatio-temporal evolution process of the multi-scale features can then be tracked by the feature clusters based on the data adaptive Fuzzy C-Means Cluster. The approach was tested with the global 0.25° satellite altimeter data over a period of 21 years from 1993 to 2013. The tracking of the multi-scale spatio-temporal evolution characteristics of the 1997-98 strong El Niño were used as validation. The results show that our method can clearly reveal and track the spatio-temporal distribution and evolution of complex geographical phenomena. Our approach is efficient for global scale data analysis, and can be used to explore the multi-scale pattern of spatio-temporal processes.

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Extraction of Spatiotemporal Association Rules for Forest Fires Prediction

Boulehmi

Ayadi

2022

Advances on Intelligent Informatics and Computing

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A spatiotemporal mining framework for abnormal association patterns in marine environments with a time series of remote sensing images

Cited by 25 publications

References 35 publications

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

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

Exploratory Method for Spatio-Temporal Feature Extraction and Clustering: An Integrated Multi-Scale Framework

Extraction of Spatiotemporal Association Rules for Forest Fires Prediction

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