Bidirectional Long Short-Term Memory Networks for Automatic Crop Classification at Regional Scale using Tabular Remote Sensing Time Series

With the arrival of European Union's new Common Agricultural Policy (CAP 2020), a paradigm shift in subsidy control is underway. Member states are required to gradually transition from a system of on-the-spot checks, where the presence or absence of a crop is detected manually on the field, to a system of agricultural monitoring based on remote sensing data; primarily - Sentinel-1 and Sentinel-2. This paper presents a classification of regional crop types based on the Bidirectional Long-Short-Term Memory (BiLSTM) network. The approach is based on tabular time series of Sentinel-1 and Sentinel-2 sensor data over the entire territory of Latvia. Two types of LSTM architectures are evaluated in this paper - regular and bidirectional. An exhaustive grid search of network hyperparameters with 15 distinct crop types led to the conclusion that the bidirectional variant of LSTM yields the highest overall weighted test accuracy of 89.1%.