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.

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.

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