Change Analysis in Registered Satellite Image Time Series

The recent proliferation of constellations of recurrent satellites enables the constitution of temporally dense times series of registered images. We therefore propose in this paper a more in depth detection and analysis of observable changes. This approach is intended to be generic and independent of the type of satellite used, whether band limited or multispectral. It is based on a global analysis of the sequence. The detection stage is based on the definition of a residual sequence calculated from the novelty filter. A statistical approach based on the NFA test is then employed to detect significant changes. We then use these detections to classify the changes according to their nature: unique or lasting. To establish the efficiency of the method, we created an open dataset of 28 sequences of 20 images acquired by Sentinel-2 in different regions of the world. We obtain satisfactory results which are consistent with the visual observations of experts.