A Multi-Level Fine-Grained Crop Classification Method Based on Multi-Expert Knowledge Distill

Crop mapping is an important task for agriculture-related activities and economic development. Most present studies focus on the crop mapping of staple crops, i.e., soybean, maize, and wheat, few concentrate on the multi-level fine-grained crop classification, which requires knowing finer crop type, i.e., barley or rye, winter wheat or spring wheat for different application. Deep learning methods with the strong ability to extract features automatically have great potential in fine-grained crop mapping. However, the classification of multi-level finer crops is challenged by the extremely similar phenological characters. In this paper, a multi-level fine-grained crop classification method based on multi-expert knowledge distill is proposed to learn the phenological features with different distinction degrees. Specifically, it uses three expert models to distinguish crop types with obvious, similar, and confusing phenological features. Then through a self-paced learning module, the student model first learns the knowledge from three expert models in the early stage and then learns to excavate the phenological features actively during the learning process. The experiment was carried on in Nordrhein Westfalen, Germany based on the EUROCROPS and time-series Sentinel-2 dataset and achieved great performance compared with popular deep learning methods.