Image Time Series Classification based on a Planar Spatio-temporal Data Representation

Image time series such as MRI functional sequences or Satellite Image Time Series (SITS) provide valuable information for the automatic analysis of complex patterns through time. A major issue when analyzing such data is to consider at the same time their temporal and spatial dimensions. In this article we present a novel data representation that makes image times series compatible with classical deep learning model, such as Convolutional Neural Networks (CNN). The proposed approach is based on a novel planar representation of image time series that converts 2D + t data as 2D images without loosing too much spatial or temporal information. Doing so, CNN can learn at the same time the parameters of 2D filters involving temporal and spatial knowledge. Preliminary results in the remote sensing domain highlight the ability of our approach to discriminate complex agricultural land-cover classes from a SITS.