Long term analysis of time series of satellite images

Guyet, Thomas; Nicolas, Hervé

doi:10.1016/j.patrec.2015.11.005

Cited by 30 publications

(19 citation statements)

References 18 publications

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“…Conversely to existing works that detect changes between only two dates, our approach accommodates with long time series data. Long time series data is useful for some applications such as reverted changes, vegetation dynamic [19], etc. As discussed in Sec.…”

Section: B Resultsmentioning

confidence: 99%

Monitoring Urban Growth with Spatial Filtering of Satellite Image Time Series

Tuna

Merciol

Lefèvre

2019

2019 Joint Urban Remote Sensing Event (JURSE)

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Monitoring urban growth and change is an important task for urban planning and disaster management. While several change detection approaches have been proposed to deal with growing urban areas, their performances are usually limited due to outliers in Satellite Image Time Series (SITS). In this study, in order to discriminate urban growth from the other changes, we exploit spatial connectivity of the changed pixels. To do so, we first stack SITS to a single synthetic image whose pixel values denote the temporal variability along the series. Then, we propose to rely on efficient and well-established spatial filtering by means of the max-tree image representation, leading to a novel approach for detecting changes in urban areas, and more precisely focusing on the spatial extent of such changes in relationship with the urban growth. Experimental results obtained on Landsat imagery of Dar es Salaam showed that our approach helps to remove outliers from the change map and provides satisfactory accuracy.

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Section: B Resultsmentioning

confidence: 99%

Monitoring Urban Growth with Spatial Filtering of Satellite Image Time Series

Tuna

Merciol

Lefèvre

2019

2019 Joint Urban Remote Sensing Event (JURSE)

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“…), the data generated by these elements are the largest of the entire available information spectrum. For this reason, the analysis of the wealth of time series has been carried out in a continuous and frequent way [33] in order to obtain the prediction variables and thus to be able to warn behaviour in the environment these occur.…”

Section: Applicationmentioning

confidence: 99%

Development of a Modelling Script of Time Series Suitable for Data Mining

Sanz‐Fernández¹,

Cabrera²,

Muñoz‐Lechuga³

et al. 2016

OJS

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Data Mining has become an important technique for the exploration and extraction of data in numerous and various research projects in different fields (technology, information technology, business, the environment, economics, etc.). In the context of the analysis and visualisation of large amounts of data extracted using Data Mining on a temporary basis (time-series), free software such as R has appeared in the international context as a perfect inexpensive and efficient tool of exploitation and visualisation of time series. This has allowed the development of models, which help to extract the most relevant information from large volumes of data. In this regard, a script has been developed with the goal of implementing ARIMA models, showing these as useful and quick mechanisms for the extraction, analysis and visualisation of large data volumes, in addition to presenting the great advantage of being applied in multiple branches of knowledge from economy, demography, physics, mathematics and fisheries among others. Therefore, ARIMA models appear as a Data Mining technique, offering reliable, robust and high-quality results, to help validate and sustain the research carried out.

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“…Specifically, vegetation indices (VI) products as time-series data have been widely employed in the remote sensing community. These data help us to understand the earth system and land-surface dynamics [4,5]. However, most of VI timeseries data was derived from low spatial resolution satellite platforms such as NOAA-AVHRR (Advanced Very High-Resolution Radiometer) instruments; EOS-MODIS (Moderate Resolution Imaging Spectro radiometer); and SPOT (Système Pour l'Observation de la Terre) VGT product [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Construction of smooth daily remote sensing time series data: a higher spatiotemporal resolution perspective

Pan

Cao

2017

Open geospatial data, softw. stand.

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Research in time-series remote sensing data is receiving increasing attention. With the availability of relatively short repeat cycle and high spatial resolution satellite data, the construction and application of high spatiotemporal remote sensing time-series data is promising. In this paper, we proposed a method to construct complete spatial time series data, with Savitzky-Golay filter for smoothing and locally-adaptive linear interpolation for generating daily NDVI imagery. An IDL-based program was developed to achieve this goal. The China's HJ-1 A/B satellite data were employed for this remote sensing time series construction. The results demonstrated that: (1) This method can generate smooth continuous time series image data successfully based on irregularly short-revisit remote sensing data; (2) HJ-1 A/B NDVI time-series were demonstrated to be successful in monitoring crop phenology and hyperspectral analysis was successfully applied on HJ-1 A/B time-series data to perform temporal endmember extraction. The IDL-based time-series construction program is generalizable for various kind of multi-temporal remote sensing data such as MODIS vegetation-index product. Discussion and concluding remarks are made to reveal the authors' perspective on higher spatial resolution time-series analysis in the remote sensing community.

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Long term analysis of time series of satellite images

Cited by 30 publications

References 18 publications

Monitoring Urban Growth with Spatial Filtering of Satellite Image Time Series

Monitoring Urban Growth with Spatial Filtering of Satellite Image Time Series

Development of a Modelling Script of Time Series Suitable for Data Mining

Construction of smooth daily remote sensing time series data: a higher spatiotemporal resolution perspective

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