The use of ALSTM-FCN for tobacco planting extraction from time-series Sentinel-1A Sar images

Spatial information on tobacco planting is crucial to many agricultural applications regarding tobacco production and management. This paper presents a deep learning model, i.e., Attention Long Short-Term Memory Fully Convolutional Network (ALSTM-FCN), to extract tobacco planting areas using time-series Sentinel-1A (S1A) SAR images. Using the ALSTM-FCN model, high-level temporal and spatial image features are fused to characterize the growth of tobacco planting. We applied the ALSTM-FCN to extract tobacco in the Fujian area using time-series S1A SAR data acquired in 2020. We compared the proposed method with a conventional LSTM and a machine learning method (e.g., Light GBM). Our results show that the extracted results by the ALSTM-FCN model have a higher extraction accuracy of 0.93 than that of the LSTM of 0.92 and the Light GBM of 0.91. We conclude that the proposed ALSTM-FCN method can be used as a promising solution for extracting tobacco using time-series SAR data in cloudy and rainy areas.