Rice Planting Area Identification Based on Multi-Temporal Sentinel-1 SAR Images and an Attention U-Net Model

Abstract

Rice is one of the most important food crops for human beings. The timely and accurate understanding of the distribution of rice can provide an important scientific basis for food security, agricultural policy formulation, and regional development planning. As an active remote sensing system, polarimetric synthetic aperture radar (PolSAR) has the advantage of working both day and night and in all weather conditions and hence plays an important role in rice growing area identification. This paper focuses on the topic of rice planting area identification using multi-temporal PolSAR images and a deep learning method. A rice planting area identification attention U-Net (RIAU-Net) model is proposed, which is trained by multi-temporal Sentinel-1 dual-polarimetric images acquired in different periods of rice growth. In addition, considering the diversity of the rice growth period in different years caused by the different climatic conditions and other factors, a transfer mechanism is investigated to apply the well-trained model to monitor the rice planting areas in different years. The experimental results show that the proposed method can significantly improve the classification accuracy, with 11–14% F1-score improvement compared with the traditional methods and a pleasing generalization ability in different years. Moreover, the classified rice planting regions are continuous. For reproducibility, the source codes of the well-trained RIAU-Net model are provided.