Nowadays, in the field of data mining, time series data analysis is a very important and challenging subject. This is especially true for time series remote sensing classification. The classification of remote sensing images is an important source of information for land resource planning and management, rational development, and protection. Many experts and scholars have proposed various methods to classify time series data, but when these methods are applied to real remote sensing time series data, there are some deficiencies in classification accuracy. Based on previous experience and the processing methods of time series in other fields, we propose a neural network model based on a self-attention mechanism and time sequence enhancement to classify real remote sensing time series data. The model is mainly divided into five parts: (1) memory feature extraction in subsequence blocks; (2) self-attention layer among blocks; (3) time sequence enhancement; (4) spectral sequence relationship extraction; and (5) a simplified ResNet neural network. The model can simultaneously consider the three characteristics of time series local information, global information, and spectral series relationship information to realize the classification of remote sensing time series. Good experimental results have been obtained by using our model.

中文翻译：

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基于自注意力机制和时间序列增强的遥感时间序列分类

如今，在数据挖掘领域，时间序列数据分析是一个非常重要且具有挑战性的主题。对于时间序列遥感分类尤其如此。遥感图像的分类是土地资源规划与管理，合理开发与保护的重要信息来源。许多专家和学者提出了各种方法来对时间序列数据进行分类，但是当将这些方法应用于真实的遥感时间序列数据时，分类精度存在一些不足。基于以往的经验和其他领域的时间序列处理方法，我们提出了一种基于自注意力机制和时间序列增强的神经网络模型，对实际的遥感时间序列数据进行分类。该模型主要分为五个部分：（1）子序列块中的存储特征提取；（2）区块之间的自我关注层；（3）时序增强；（4）频谱序列关系提取；（5）简化的ResNet神经网络。该模型可以同时考虑时间序列本地信息，全局信息和频谱序列关系信息的三个特征，以实现遥感时间序列的分类。通过使用我们的模型已经获得了良好的实验结果。利用光谱序列关系信息实现遥感时间序列的分类。通过使用我们的模型已经获得了良好的实验结果。利用光谱序列关系信息实现遥感时间序列的分类。通过使用我们的模型已经获得了良好的实验结果。