Spatially explicit active learning for crop-type mapping from satellite image time series

The availability of sufficient annotated samples is one of the main challenges of the supervised methods used to classify crop types from remote sensing images. Generating a large number of annotated samples is a time-consuming and expensive task. Active Learning (AL) is one of the solutions that can be used to optimize the sample annotation, resulting in an efficiently trained supervised method with less effort. Unfortunately, most of the developed AL methods do not account for the spatial information inherent in remote-sensing images. We propose a novel spatially-explicit AL that uses a semi-variogram to identify and discard the spatially adjacent and, consequently, redundant samples. It was evaluated using Random Forest (RF) and Sentinel-2 Satellite Image Time Series (SITS) in two study areas from the Netherlands and Belgium. In the Netherlands, the spatially explicit AL selected a total number of 97 samples as being relevant for the classification task which led to an overall accuracy of 80%, while the traditional AL method selected a total number of 169 samples achieving an accuracy of 82%. In Belgium, spatially explicit AL selected 223 samples and obtained an overall accuracy of 60%, compared to the traditional AL that selected 327 samples which yielded an accuracy of 63%. We concluded that the developed AL method helped RF achieve a good performance mostly for the classes consisting of individual crops with a relatively distinctive growth pattern such as sugar beets or cereals. Aggregated classes such as ‘fruits and nuts’ represented, however, a challenge. The proposed AL method reveals that accounting for spatial information is an efficient solution to map target crops since it facilitates high accuracy with a low number of samples and, consequently, lower computational resources and time and financial resources for annotation.