Vineyard detection from multitemporal Sentinel-2 images with a Transformer model

This study introduces an innovative method for vineyard detection by integrating advanced machine learning techniques with high-resolution satellite imagery, particularly focusing on the use of preprocessed multitemporal Sentinel-2 images combined with a Transformer-based model.

We collected a series of Sentinel-2 images over an entire seasonal cycle from eight distinct locations in Oregon, United States, all within similar climatic zones. The training and validation database sizes are 403612 and 100903, respectively. To reduce the cloud effect, we used the monthly median band values derived from initially cloud-filtered images.  The multispectral (12 bands) and multiscale (10m, 20m, and 60m) time series were effective in capturing both the phenological patterns of the land covers and the overall management activities.

The Transformer model, primarily recognized for its successes in natural language processing tasks, was adapted for our time series identification scenario. Then, we transferred the object detection into a binary classification task. Our findings demonstrate that the Transformer model significantly surpasses traditional 1D convolutional neural networks (CNNs) in detecting vineyards across 16 new areas within similar climatic zones, boasting an impressive accuracy of 87.77% and an F1 score of 0.876. In the majority of these new test locations, the accuracy exceeded 92%, except for two areas that experienced significant cloud interference and presented numerous missing values in their time series data. This model proved its capability to differentiate between land covers with similar characteristics during various stages of growth throughout the season. Compared with attention LSTM and BiLSTM, it has less trainable parameters when getting a similar performance. The model was especially adept at handling temporal variations, elucidating the dynamic changes in vineyard phenology over time. This research underscores the potential of combining advanced machine learning techniques with high-resolution satellite imagery for crop type detection and suggests broader applications in land cover classification tasks. Future research will pay more attention to the missing value problem.