Ranking evolution maps for Satellite Image Time Series exploration: application to crustal deformation and environmental monitoring

Méger, Nicolas; Rigotti, Christophe; Pothier, Catherine; Nguyen, Truyen; Lodge, Felicity; Gueguen, Lionel; Andreoli, Rémi; Doin, Marie‐Pierre; Datcu, Mihai

doi:10.1007/s10618-018-0591-9

Cited by 8 publications

(9 citation statements)

References 84 publications

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“…The efficient extraction of these local shapes is based on a pattern mining technique (Méger et al 2019) adapted here to handle spectral profiles. Examples of shapes that can be retrieved from these profiles, include (but are not limited to) plateau (flat zone), peak, valley.…”

Section: Discrete Spectral Profile Analysismentioning

confidence: 99%

Studying Evolution of Hydrothermal Alteration Materials in the Turrialba Volcano Trough Multispectral and Hyperspectral Images

Rejas

Pothier

Rigotti

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The aim of this work is to develop a geospatial methodology for the analysis of the time evolution of The Turrialba volcano using different automatic imaging techniques compared to expert-based remote sensing techniques. Change detection of hydrothermal alteration materials in relation with time series from multisensor data acquired in spectral ranges of the visible (VIS) and short wave infrared (SWIR) have been calculated. We used for this purpose multispectral and hyperspectral scenes of the Sentinel 2, ALI and Hyperion sensors, respectively, on four dates from 2013 and 2018. This work adopts a multi-source approach, applied to the analysis of the correlations between hydrothermal materials and spectral anomalies in The Turrialba volcano complex, located in The Central Volcanic Range (Costa Rica).An expert-based technique called Crosta’s technique for detecting hydrothermal materials have been applied. We have chosen four variables for generating a different Principal Component Analysis (PCA) for groups of channels, two highly reflective and two highly absorptive for each mineral. We have tested another technique to detect hydrothermal materials based on a discrete spectral profile analysis and an unsupervised data mining approach. In other sense, we have applied an automatic technique called anomaly detection to compare with the hydrothermal materials results. Results are presented as an approach based on a comparison of two different strategies whose main future interest lies in the automated identification of patterns of hydrothermally altered materials without prior knowledge or poor information about the area, which has relevant implications in image-based prospecting.

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Section: Discrete Spectral Profile Analysismentioning

confidence: 99%

Studying Evolution of Hydrothermal Alteration Materials in the Turrialba Volcano Trough Multispectral and Hyperspectral Images

Rejas

Pothier

Rigotti

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Unsurprisingly, Satellite image time series (SITS) analysis continues to gain popularity in the literature. Such data combine spatial and temporal dimensions and they can be used for many problems such as assessing the temporal evolution of phenomena or objects (Méger et al, 2019). Supervised classification algorithms currently represent the state-of-the-art in automatic mapping and monitoring of our planet, e.g.…”

Section: Motivationmentioning

confidence: 99%

Spatio-Temporal Object Stability for Monitoring Evolving Areas in Satellite Image Time Series

Tuna

Merciol

Lefèvre

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Monitoring observable processes in Satellite Image Time Series (SITS) is one of the crucial way to understand dynamics of our planet that is facing unexpected behaviors due to climate change. In this paper, we propose a novel method to assess the evolution of objects (and especially their surface) through time. To do so, we first build a space-time tree representation of image time series. The so-called space-time tree is a hierarchical representation of an image sequences into a nested set of nodes characterizing the observed regions at multiple spatial and temporal scales. Then, we measure for each node the spatial area occupied at each time sample, and we focus on its evolution through time. We thus define the spatio-temporal stability of each node. We use this attribute to identify and measure changing areas in a remotely-sensed scene. We illustrate the purpose of our method with some experiments in a coastal environment using Sentinel-2 images, and in a flood occurred area with Sentinel-1 images.

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“…This interface also gives access to complementary visualization modes and statistics about the pattern occurrence dates (histograms, median values, etc.). The reader is referred to [10] and [11] for the complete definition of the patterns and their maps, the description of the extraction/ranking steps and the guidelines for parameter settings.…”

Section: System Descriptionmentioning

confidence: 99%

“…A first proposal has been recently published in [10]. It is based on the extraction of Grouped Frequent Sequential pattern (GFS-patterns) as defined in [11], for which a notion of pattern reliability is proposed. Such reliable patterns are the ones for which the confidence measures, at the occurrence level, are sufficiently high on average.…”

Section: Introductionmentioning

confidence: 99%

“…Such reliable patterns are the ones for which the confidence measures, at the occurrence level, are sufficiently high on average. Once extracted, patterns are ranked according to the swap randomization method described in [11]. This paper presents the corresponding system, DFTS-P2miner (DFTS-Pattern maP miner), that implements the method of [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Pattern-Based Mining System for Exploring Displacement Field Time Series

Nguyen

Méger

Rigotti

et al. 2019

2019 International Conference on Data Mining Workshops (ICDMW)

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This paper presents the first available system for mining patterns from Displacement Field Time Series (DFTS) along with the confidence measures inherent to these series. It consists of four main modules for data preprocessing, pattern extraction, pattern ranking and pattern visualization. It is based on an efficient extraction of reliable grouped frequent sequential patterns and on swap randomization. It can be for example used to assess climate change impacts on glacier dynamics.

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Ranking evolution maps for Satellite Image Time Series exploration: application to crustal deformation and environmental monitoring

Cited by 8 publications

References 84 publications

Studying Evolution of Hydrothermal Alteration Materials in the Turrialba Volcano Trough Multispectral and Hyperspectral Images

Studying Evolution of Hydrothermal Alteration Materials in the Turrialba Volcano Trough Multispectral and Hyperspectral Images

Spatio-Temporal Object Stability for Monitoring Evolving Areas in Satellite Image Time Series

A Pattern-Based Mining System for Exploring Displacement Field Time Series

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