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.

中文翻译：

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一种融合 CNN 和 RNN 的双路径模型，具有注意力机制，用于作物分类


快速准确的作物分类对于估计作物信息和改善农田管理至关重要。利用时间序列数据进行作物分类的深度学习模型的应用已成为最有前途的方法。然而，大多数方法依赖单一模型进行数据处理，导致分类精度较低且稳定性较差。因此，本研究提出了一种带有注意机制的双路径方法（DPACR），以提高该模型架构在使用时间序列数据进行作物分类中的性能。具体来说，该模型包括两个分支：具有自注意力机制的双向门控循环单元（GRU）的循环神经网络（RNN）分支，以及基于SE-ResNet的卷积神经网络（CNN）分支。作物分类由主分类器完成，并由两个分支的辅助分类器支持。利用 Google Earth Engine 和 Sentinel-2 卫星数据，DPACR 在内蒙古河套灌区进行了测试。对比实验表明，与其他五种模型（MLP、SE-ResNet、Bi-At-GRU、SVM 和 RF）相比，DPACR 实现了最高的总体精度（OA = 0.959）和 Kappa 系数（Kappa = 0.941）。 DPACR 在对六种作物进行分类方面表现出色，在多个类别中保持了高精度。与注意力机制相比，辅助分类器可以显着提高分类性能。该研究突出了云计算和深度学习的有效结合进行大规模农作物分类，为农业监测和管理提供了实用方法。