Solution for crop classification in regions with limited labeled samples: deep learning and transfer learning

Abstract

Reliable classification results are crucial for guiding agricultural production, forecasting crop yield, and ensuring food security. Generating reliable classification results is relatively simple in regions with sufficient labeled samples, but regions with limited labeled samples remain a significant challenge. In this study, we propose two new solutions that leverage the feature representation capabilities of deep learning and the sample reuse potential of transfer learning to solve the limited label problem. Specifically, we develop a Variable-dimensional Symmetric Network with Position Encoding (VPSNet) to improve the efficiency of labeled sample utilization. Additionally, we introduce a transfer strategy based on the Inter-Regional Discrepancies in Crop Time Series (IRDCTS) to expand the labeled sample reuse region. We evaluated the proposed model in three regions with limited labels between 2017 and 2018. Experimental results show that our model has superior discriminative feature extraction capabilities compared to other existing models. The feasibility of the proposed transfer strategy is tested in three pair regions, showing that IRDCTS can enhance the model adaptability by reducing inter-domain discrepancies. This study provides a comprehensive solution to the classification problem of the limited labeled samples, involving both the development of classification models and the implementation of transfer strategies.

Keywords

• deep learning
• transfer learning
• limited labeled sample
• crop classification
• time series