Sequence Similarity and Multi-Date Image Segmentation

Ketterlin, Alain; Gançarski, Pierre

doi:10.1109/multitemp.2007.4293034

Cited by 9 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These dissimilarities take into account the time warping where Euclidean distance focus attention on the date of the changes in time series. Ketterlin and Gancarski (2007) proposed to use such elastic distance to cluster the pixels of multi-date images with presence of clouds (missing values in time series). Then, Petitjean et al (2011) proposed the DBA algorithm, a modified K-Means algorithm to cluster time-series using the DTW, and applied it to SITS.…”

Section: Related Workmentioning

confidence: 99%

Long term analysis of time series of satellite images

Guyet

Nicolas

2016

Pattern Recognition Letters

View full text Add to dashboard Cite

International audienceSatellite images allow the acquisition of large-scale ground vegetation. Images are available along several years with a high acquisition rate. Such data are called satellite image time series (SITS). We present a method to analyse an SITS through the characterization of the evolution of a vegetation index (NDVI) at two scales: annual and multi-annual. We evaluate our method on SITS of the Senegal from 2001 to 2008 and we compare our method to a clustering of long time series. The results show that our method better discriminates regions in the median zone of Senegal and locates fine interesting areas

show abstract

Section: Related Workmentioning

confidence: 99%

Long term analysis of time series of satellite images

Guyet

Nicolas

2016

Pattern Recognition Letters

View full text Add to dashboard Cite

show abstract

“…They include methods such as post-classification comparison [16], linear data transformation (Principal Component Analysis and Maximum Autocorrelation Factor) [17], image regression or interpolation [18] and frequency analysis (e.g., Fourier, wavelet) [19]. Finally, we find methods designed towards image time series and based on radiometric trajectory analysis [20]. Whatever the type of methods used in order to analyze satellite image time series, there is a gap between the amount of data representing these time series, and the ability of algorithms to analyze them.…”

Section: Related Work: Sits Analysismentioning

confidence: 99%

Discovering Significant Evolution Patterns From Satellite Image Time Series

Petitjean

Masséglia

Gançarski

et al. 2011

Int. J. Neur. Syst.

View full text Add to dashboard Cite

Satellite Image Time Series (SITS) provide us with precious information on land cover evolution. By studying these series of images we can both understand the changes of specific areas and discover global phenomena that spread over larger areas. Changes that can occur throughout the sensing time can spread over very long periods and may have different start time and end time depending on the location, which complicates the mining and the analysis of series of images. This work focuses on frequent sequential pattern mining (FSPM) methods, since this family of methods fits the above-mentioned issues. This family of methods consists of finding the most frequent evolution behaviors, and is actually able to extract long-term changes as well as short term ones, whenever the change may start and end. However, applying FSPM methods to SITS implies confronting two main challenges, related to the characteristics of SITS and the domain's constraints. First, satellite images associate multiple measures with a single pixel (the radiometric levels of different wavelengths corresponding to infra-red, red, etc.), which makes the search space multi-dimensional and thus requires specific mining algorithms. Furthermore, the non evolving regions, which are the vast majority and overwhelm the evolving ones, challenge the discovery of these patterns. We propose a SITS mining framework that enables discovery of these patterns despite these constraints and characteristics. Our proposal is inspired from FSPM and provides a relevant visualization principle. Experiments carried out on 35 images sensed over 20 years show the proposed approach makes it possible to extract relevant evolution behaviors.

show abstract

“…Beside the object-based techniques (e.g., [1]), the pixel-based techniques, focusing either on a specific type of evolution, i.e. change detection techniques (e.g., [2]) or on the characterization of the whole sequence of pixel values (e.g., [3]) have also been proposed. None of these techniques can extract sets of grouped pixels which share a common evolution without first extracting features [1] and/or without making any assumption about the type of evolution [2].…”

Section: Introductionmentioning

confidence: 99%

Urban area monitoring by sequential patterns extracted from multi-temporal satellite data using connectivity measures

Julea

Ionescu

et al. 2014

2014 IEEE Geoscience and Remote Sensing Symposium

View full text Add to dashboard Cite

The temporal evolution of pixel values in Satellite Image Time Series (SITS) is considered criterion for the characterization, discrimination and identification of terrestrial objects and phenomena. Due to the exponential behavior of sequences number with specialization, Sequential Data Mining techniques need to be applied. The spatial aspect of the data was taken into account by the introduction of connectivity measures that characterize the pixels tendency to form objects. The conjunction of corresponding Connectivity Constraints (CC) with the Support Constraint (SC) leads to the extraction of Grouped Frequent Sequential Patterns (GFSP), a concept with proved capability for preliminary description and localization of terrestrial events. This work is focused on efficient SITS extraction of evolutions that fulfil SC and CC. Experiments performed on Bucharest urban interferometric SITS are used to illustrate the potential of the approach to find interesting evolution patterns.

show abstract

Sequence Similarity and Multi-Date Image Segmentation

Cited by 9 publications

References 4 publications

Long term analysis of time series of satellite images

Long term analysis of time series of satellite images

Discovering Significant Evolution Patterns From Satellite Image Time Series

Urban area monitoring by sequential patterns extracted from multi-temporal satellite data using connectivity measures

Contact Info

Product

Resources

About