  The increasing spatial and temporal resolution of globally available
satellite images, such as provided by Sentinel-2, creates new possibilities for
researchers to use freely available multi-spectral optical images, with
decametric spatial resolution and more frequent revisits for remote sensing
applications such as land cover and crop classification (LC&CC), agricultural
monitoring and management, environment monitoring. Existing solutions dedicated
to cropland mapping can be categorized based on per-pixel based and
object-based. However, it is still challenging when more classes of
agricultural crops are considered at a massive scale. In this paper, a novel
and optimal deep learning model for pixel-based LC&CC is developed and
implemented based on Recurrent Neural Networks (RNN) in combination with
Convolutional Neural Networks (CNN) using multi-temporal sentinel-2 imagery of
central north part of Italy, which has diverse agricultural system dominated by
economic crop types. The proposed methodology is capable of automated feature
extraction by learning time correlation of multiple images, which reduces
manual feature engineering and modeling crop phenological stages. Fifteen
classes, including major agricultural crops, were considered in this study. We
also tested other widely used traditional machine learning algorithms for
comparison such as support vector machine SVM, random forest (RF), Kernal SVM,
and gradient boosting machine, also called XGBoost. The overall accuracy
achieved by our proposed Pixel R-CNN was 96.5%, which showed considerable
improvements in comparison with existing mainstream methods. This study showed
that Pixel R-CNN based model offers a highly accurate way to assess and employ
time-series data for multi-temporal classification tasks.
