气候变化领域集成服务门户(CCPortal): Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series-A Case Study in Zhanjiang, China

CCPortal

DOI	10.3390/rs11222673
	Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series-A Case Study in Zhanjiang, China
	Zhao, Hongwei; Chen, Zhongxin; Jiang, Hao; Jing, Wenlong; Sun, Liang; Feng, Min
通讯作者	Jiang, H (通讯作者)
发表日期	2019
EISSN	2072-4292
卷号	11 期号:22
英文摘要	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 affected 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 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.
关键词	RANDOM FOREST NETWORK SYSTEM FIELDS AREA
英文关键词	early crop classification; one-dimensional CNNs; long short-term memory; gated recurrent unit; incremental classification; RNNs; synthetic aperture radar; Sentinel-1A
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000502284300078
来源期刊	REMOTE SENSING
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259603
推荐引用方式 GB/T 7714	Zhao, Hongwei,Chen, Zhongxin,Jiang, Hao,et al. Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series-A Case Study in Zhanjiang, China[J]. 中国科学院青藏高原研究所,2019,11(22).
APA	Zhao, Hongwei,Chen, Zhongxin,Jiang, Hao,Jing, Wenlong,Sun, Liang,&Feng, Min.(2019).Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series-A Case Study in Zhanjiang, China.REMOTE SENSING,11(22).
MLA	Zhao, Hongwei,et al."Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series-A Case Study in Zhanjiang, China".REMOTE SENSING 11.22(2019).