Recurrent Deep Learning for Rice Fields Detection from SAR Images

• Published in: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium pp. 1548 - 1551
• Main Authors: Wu, Meng-Che, Alkhaleefah, Mohammad, Chang, Lena, Chang, Yang-Lang, Shie, Ming-Hwang, Liu, Shian-Jing, Chang, Wen-Yen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.09.2020
• Subjects: Agriculture; Deep learning; Logic gates; Monitoring; Radar polarimetry; Remote sensing; rice fields detection; SAR images; Synthetic aperture radar; Time series analysis; time series data
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS39084.2020.9324337

Rice is one of the most important and valuable crops in the world. People around the world mainly depend on rice as their daily diet. Therefore, efficient rice fields monitoring is a crucial factor in the improvement of rice crop yield estimation, damage evaluation, budget planning, and agricultural resource management. Synthetic aperture radar (SAR) is an effective tool in monitoring agricultural fields because of its ability to provide high resolution images regardless of weather conditions. However, precision agriculture has put higher requirements for SAR data analysis. In recent years, deep learning methods have achieved great success in various remote sensing applications. In this research, two of the most popular deep learning architectures for time series data, namely convolutional long short-term memory (ConvLSTM) and gated recurrent unit (GRU) have been explored and applied to detect rice fields from SAR images in Taiwan. The experimental results showed that time-series deep learning methods for analyzing SAR data have a great potential for improving the rice fields detection.