## 1 Introduction
Traditional classification methods are classified into supervised classification and unsupervised classification. The representative of the unsupervised classification is clustering algorithm such as IOSDATA [27], Maximum Likelihood Classification [28] and K-Means [29]. The supervised classification is mostly machine learning algorithms. For example, [PERSON] et al. used the ANN to get the Fuzzy ARTMAP [30], [PERSON] et al. used the IOSDATA algorithm to obtain the Temperate East Asia classification map [31], [PERSON] et al. completed the NLCD 2001 by using the Decision Tree [32], and [PERSON] et al. used the SVM algorithm to produce land classification maps [33]. These algorithms currently used in existing land cover classification products are more complex or require more manual intervention and manual extraction [34; 35], and still need a lot of reference data [36]. In recent years, deep learning [37; 38] has demonstrated the excellent performance of neural network models in remote sensing classification [39; 40; 41]. A very typical application is the use of convolutional neural networks (CNN) for land cover classification [42; 43]. However, the characteristics of CNN indicate that they are more suitable for processing remotely sensed images with strong spatial correlation. These models cannot process time series information, and each attribute is an independent individual during the classification process. For the time characteristics of quantitative remote sensing products with strong temporal correlation, this method does not prove advantageous in classification. The key to long time series land cover classification is determining how to use time series to make full use of rich seasonal patterns and order relationships for classification task. Recurrent neural networks (RNNs), especially long short-term memory networks [44] (LSTM), capture time correlation very well [45]. Therefore, RNN are generally considered to be a good machine learning method for time series and land cover change studies [46; 47]. Existing research has shown that the LSTM model has higher accuracy for time series classification than does CNN, and the accuracy is far better than that of SVM [48]. The main goal of this study is to establish a long time series classification data extraction model based on the method of deep learning using long time series quantitative remote sensing products as the input of the model, which led to this study of long time series land cover classification. By comparing the difference between the traditional method and the LSTM model, we propose a Bi-LSTM-based land cover classification method for multi-temporal remote sensing classification. Furthermore, learning from imbalanced training data is a common problem [49]. Sample imbalances are common in remote sensing land cover classifications, and rare categories may be insufficient in number compared to large categories. Studies have shown that balanced data sets have a positive impact on classification results [50]. SMOTE [51] can artificially synthesize new samples by interpolation. Since the SMOTE algorithm may excessively increases the order of magnitude of rare samples, we have made some algorithmic improvements. The 10 categories were divided into three layers by magnitude. SMOTE was used for upsampling in each layer. We combine SMOTE with stratified sampling, and the resulting land cover classification map generated by the model is closer to the actual situation. This study is divided into six parts. After the introduction of Section 1, the data used in this time series classification model and related information are introduced in Section 2. Section 3 describes the model architecture used for deep learning to classify and optimize the model. Then, Section 4 summarizes and analyzes the results this study, and calculates the accuracy of the model and the accuracy and reliability of the time trend. Section 5 clarifies the views of this study and explains our results. Finally, Section 6 summarizes the paper.