Domain-Invariant Crop Type Mapping Using Transformer-Based Time-Frequency Feature Extraction and Adaptation for Unlabeled Target Regions

Developing near-accurate crop type mapping is crucial for effective agricultural monitoring. However, this is often challenged by the lack of labeled data in many regions and the issue of domain shift. To address these challenges, we propose a domain adaptation framework that leverages transformer-based time-frequency feature extraction to create domain-invariant representations, enabling reliable crop type mapping in unlabeled target regions. The framework incorporates a transformer encoder along with a frequency encoder to capture the temporal and frequency-domain pattern of the time series. We use three main loss functions, namely the Reconstruction loss, the Classification loss and the Alignment loss during the training of the network, which ensures the model simultaneously optimizes the classification accuracy, feature integrity, and the distribution between source and target. This comprehensive optimization ensures that the model learns robust, domain-invariant features that generalize well across different regions. The proposed method demonstrates strong performance in predicting crop types in regions without labeled data, providing a powerful and adaptable tool for global agricultural monitoring and decision-making. Additionally, we demonstrate how the accuracy improves compared to direct transfer learning-based approaches, showcasing the superiority of domain adaptation strategies in crop-type mapping using remote sensing.