This paper presented a state-of-the-art framework, Time Gated Convolutional Neural Network (TGCNN) that takes advantage of temporal information and gating mechanisms for the crop classification problem. Besides, several vegetation indices were constructed to expand dimensions of input data to take advantage of spectral information. Both spatial (channel-wise) and temporal (step-wise) correlation are considered in TGCNN. Specifically, our preliminary analysis indicates that step-wise information is of greater importance in this data set. Lastly, the gating mechanism helps capture high-order relationship. Our TGCNN solution achieves $0.973$ F1 score, $0.977$ AUC ROC and $0.948$ IoU, respectively. In addition, it outperforms three other benchmarks in different local tasks (Kenya, Brazil and Togo). Overall, our experiments demonstrate that TGCNN is advantageous in this earth observation time series classification task.

翻译：本文提出了一个最先进的框架,即利用作物分类问题的时间信息和标记机制的时代革命神经网络(TGCNN),此外,为了扩大投入数据的方方面面以利用光谱信息,还设计了一些植被指数,在TGCN中考虑了空间(通道)和时间(分步)的相互关系。具体地说,我们的初步分析表明,在这一数据集中,分步骤信息更为重要。最后,标记机制有助于捕捉高级关系。我们的TGCNN解决方案分别取得了0.973美元的F1分、0.977美元的AUC ROC和0.948美元的IoU。此外,它超越了不同地方任务(肯尼亚、巴西和多哥)的其他三个基准。总的来说,我们的实验表明,TGCNN在地球观察时间序列分类任务中具有优势。