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The increasing spatial and temporal resolution of globally availablesatellite images, such as provided by Sentinel-2, creates new possibilities forresearchers to use freely available multi-spectral optical images, withdecametric spatial resolution and more frequent revisits for remote sensingapplications such as land cover and crop classification (LC&CC), agriculturalmonitoring and management, environment monitoring. Existing solutions dedicatedto cropland mapping can be categorized based on per-pixel based andobject-based. However, it is still challenging when more classes ofagricultural crops are considered at a massive scale. In this paper, a noveland optimal deep learning model for pixel-based LC&CC is developed andimplemented based on Recurrent Neural Networks (RNN) in combination withConvolutional Neural Networks (CNN) using multi-temporal sentinel-2 imagery ofcentral north part of Italy, which has diverse agricultural system dominated byeconomic crop types. The proposed methodology is capable of automated featureextraction by learning time correlation of multiple images, which reducesmanual feature engineering and modeling crop phenological stages. Fifteenclasses, including major agricultural crops, were considered in this study. Wealso tested other widely used traditional machine learning algorithms forcomparison such as support vector machine SVM, random forest (RF), Kernal SVM,and gradient boosting machine, also called XGBoost. The overall accuracyachieved by our proposed Pixel R-CNN was 96.5%, which showed considerableimprovements in comparison with existing mainstream methods. This study showedthat Pixel R-CNN based model offers a highly accurate way to assess and employtime-series data for multi-temporal classification tasks.

applied sciences

Improvement in Land Cover and Crop Classification based on Temporal  Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural  Network (R-CNN)