Combining pixel- and object-level information for land-cover mapping using time-series of Sentinel-2 satellite data

In this letter, we propose a new methodology for Satellite Image Time Series (SITS) land cover mapping, named Two Branches Convolutional Neural Network (TwoBCNN). The main objective of the proposed methodology is to combine pixel- and object-level multi-variate time-series information in the classification process. Experiments were carried out on a study site located in the south-west of France, namely, Dordogne leveraging Sentinel-2 SITS data. Results are compared to those obtained by several standard used approaches to deal with SITS-based land cover mapping. Results demonstrate that TwoBCNN, based on a combination of pixel- and object-based information, achieved the highest classification performance with respect to the competing approaches.