Long term analysis of time series of satellite images

•We present the challenges of processing long-term satellite image time-series (SITS).•We propose a multi-time-scale strategy to classify images.•We illustrate our approach on a large SITS stack.•Experimental results show the effectiveness of the proposed method. [Display omitted] Satellite images a...

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Bibliographic Details
Published in:Pattern recognition letters 2016-01, Vol.70, p.17-23
Main Authors: Guyet, Thomas, Nicolas, Hervé
Format: Article
Language:English
Subjects:
Clustering
Computer Science
Data Structures and Algorithms
Image Processing
Remote sensing
Time series
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Summary:•We present the challenges of processing long-term satellite image time-series (SITS).•We propose a multi-time-scale strategy to classify images.•We illustrate our approach on a large SITS stack.•Experimental results show the effectiveness of the proposed method. [Display omitted] Satellite 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 characterisation 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.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2015.11.005