Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China
Authors: Hongwei Zhao, Zhongxin Chen, Hao Jiang, Wenlong Jing, Liang Sun, Min Feng 6
Date: 2019-11-15 00:00:00
Proceeding: Remote Sensing (ISSN: 2072-4292), MDPI 2019 11
Tasks: Classification, Crop Classification, General Classification, Time Series, Time Series Analysis

Timely and accurate estimation of the area and distribution of crops is vital for food security.
Optical remote sensing has been a key technique for acquiring crop area and conditions on regional
to global scales, but great challenges arise due to frequent cloudy days in southern China. This
makes optical remote sensing images usually unavailable. Synthetic aperture radar (SAR) could
bridge this gap since it is less aeffected by clouds. The recent availability of Sentinel-1A (S1A) SAR
imagery with a 12-day revisit period at a high spatial resolution of about 10 m makes it possible to
fully utilize phenological information to improve early crop classification. In deep learning methods,
one-dimensional convolutional neural networks (1D CNNs), long short-term memory recurrent
neural networks (LSTM RNNs), and gated recurrent unit RNNs (GRU RNNs) have been shown
to efficiently extract temporal features for classification tasks. However, due to the complexity of
training, these three deep learning methods have been less used in early crop classification. In this
work, we attempted to combine them with an incremental classification method to avoid the need for
training optimal architectures and hyper-parameters for data from each time series. First, we trained
1D CNNs, LSTM RNNs, and GRU RNNs based on the full images’ time series to attain three classifiers
with optimal architectures and hyper-parameters. Then, starting at the first time point, we performed
an incremental classification process to train each classifier using all of the previous data, and obtained
a classification network with all parameter values (including the hyper-parameters) at each time
point. Finally, test accuracies of each time point were assessed for each crop type to determine the
optimal time series length. A case study was conducted in Suixi and Leizhou counties of Zhanjiang
City, China. To verify the effectiveness of this method, we also implemented the classic random forest
(RF) approach. The results were as follows: (i) 1D CNNs achieved the highest Kappa coefficient
(0.942) of the four classifiers, and the highest value (0.934) in the GRU RNNs time series was attained
earlier than with other classifiers; (ii) all three deep learning methods and the RF achieved F measures
above 0.900 before the end of growth seasons of banana, eucalyptus, second-season paddy rice, and
sugarcane; while, the 1D CNN classifier was the only one that could obtain an F-measure above 0.900
for pineapple before harvest. All results indicated the eeffectiveness of the solution combining the
deep learning models with the incremental classification approach for early crop classification. This
method is expected to provide new perspectives for early mapping of croplands in cloudy areas.