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A dual-path model merging CNN and RNN with attention mechanism for crop classification

文献类型：期刊论文

作者Zhang, Fuyao1,2,3,4; Yin, Jielin2,3; Wu, Nan2,3; Hu, Xinyu2,3; Sun, Shikun2,3; Wang, Yubao2,3
刊名EUROPEAN JOURNAL OF AGRONOMY
出版日期2024-09-01
卷号159页码:127273
关键词Crop classification Deep learning Google Earth Engine Attention mechanism Time-series data
DOI10.1016/j.eja.2024.127273
产权排序3
文献子类Article
英文摘要Rapid and accurate crop classification is essential for estimating crop information and improving cropland management. The application of deep learning models for crop classification using time-series data has become the most promising method. However, most approaches rely on single models for data processing result in lower classification accuracy and poor stability. Therefore, this study proposes a dual-path approach with attention mechanisms (DPACR) to promote the performance of this model architecture in crop classification using time series data. Specifically, the model comprises two branches, the Recurrent neural network (RNN) branch with bidirectional gated recurrent units (GRU) with a self-attention mechanism, and the convolutional neural network (CNN) branch based on SE-ResNet. Crop classification is accomplished by a main classifier, supported by auxiliary classifiers from the two branches. Using the Google Earth Engine and the Sentinel-2 satellite data, DPACR was tested in the Hetao irrigation district in Inner Mongolia, China. The comparison experiment demonstrated that the DPACR achieved the highest overall accuracy (OA = 0.959) and Kappa coefficient (Kappa = 0.941) compared to other five models (MLP, SE-ResNet, Bi-At-GRU, SVM, and RF). DPACR excelled in classifying six crops, maintaining high accuracy across multiple classes. Compared to attention mechanisms, auxiliary classifiers can significantly improve classification performance. This study highlights the effective combination of cloud computing and deep learning for large-scale crop classification, providing a practical method for agricultural monitoring and management.
WOS关键词INDEX ; EXTENT ; WATER
WOS研究方向Agriculture
WOS记录号WOS:001269535300001
源URL[http://ir.igsnrr.ac.cn/handle/311030/206039]  
专题陆地表层格局与模拟院重点实验室_外文论文
通讯作者Wang, Yubao
作者单位1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
2.Northwest A&F Univ, Minist Educ, Key Lab Agr Soil & Water Engn Arid & Semiarid Area, Yangling 712100, Shaanxi, Peoples R China
3.Northwest A&F Univ, Inst Water Saving Agr Arid Reg China, Yangling 712100, Shaanxi, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Fuyao,Yin, Jielin,Wu, Nan,et al. A dual-path model merging CNN and RNN with attention mechanism for crop classification[J]. EUROPEAN JOURNAL OF AGRONOMY,2024,159:127273.
APA Zhang, Fuyao,Yin, Jielin,Wu, Nan,Hu, Xinyu,Sun, Shikun,&Wang, Yubao.(2024).A dual-path model merging CNN and RNN with attention mechanism for crop classification.EUROPEAN JOURNAL OF AGRONOMY,159,127273.
MLA Zhang, Fuyao,et al."A dual-path model merging CNN and RNN with attention mechanism for crop classification".EUROPEAN JOURNAL OF AGRONOMY 159(2024):127273.

入库方式： OAI收割

来源：地理科学与资源研究所

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