论文标题
时间门控卷积神经网络用于作物分类
Time Gated Convolutional Neural Networks for Crop Classification
论文作者
论文摘要
本文提出了一个最先进的框架，时间是封闭式卷积神经网络（TGCNN），该框架利用时间信息和门控机制来解决农作物分类问题。此外，构建了几个植被指数以扩大输入数据的维度以利用光谱信息。 TGCNN中都考虑了空间（通道）和时间（逐步）相关性。具体而言，我们的初步分析表明，在此数据集中，逐步信息更为重要。最后，门控机制有助于捕获高阶关系。我们的TGCNN解决方案分别达到$ 0.973 $ F1分数，$ 0.977 $ AUC ROC和$ 0.948 $ iou。此外，它在不同的本地任务（肯尼亚，巴西和多哥）中的其他三个基准优于其他三个基准。总体而言，我们的实验表明TGCNN在地球观察时间序列分类任务中是有利的。
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.
