## 4 Results
### Long Time Series Classification Model Results  
After inputting the dataset obtained from the layered SMOTE algorithm into the Bi-LSTM model, the model achieves an optimal state through multiple iterations. Therefore, the long time series land cover classification model is obtained. Generally speaking, for a deep learning model, the training accuracy of the model will continue to rise as iterations increase. When the model is trained to a certain extent, the model may have an over-fitting ([PERSON] et al., 2017). At this point, the generalization of the model will be reduced. The model works well on the training set, but it works poorly on the test set. So it is necessary to input the validation set to observe the test accuracy of the model ([PERSON] et al., 2017). In the learning curve, if the training accuracy and the validation accuracy are far apart, the variance of the model is large (in general, the training accuracy is higher than the validation accuracy), and the model is over-fitting. Ideally, the two precision curves of the training set and the validation set are close together. Once the classification accuracy of the validation set is saturated, the model is already in an optimal state. In the process of model training, the training precision curve, validation accuracy curve and loss function curve of the model are also depicted, as shown in Figure 6.  
At the same time, the choice of the sample size of the training sample also affects the accuracy of the model. If the sample size is too small, the features of samples are not representative. In addition, if the sample size is too large, it will lead to an increase in the training time. To save training time and ensure training efficiency and training accuracy, we conducted experiments on the influence of different sample sizes on the accuracy of the model. Finally, we found that when we select 80% of the sample size as the training sample (Figure 7), the training effect of the model can achieve the best results.  
Figure 6: Model training accuracy curve, validation accuracy curve and loss curve.  
Figure 7: Effect of the sample size on the model accuracy.  
The final test accuracy of the model is 84.2%. After inputting the test set into the model, the confusion matrix of the test set classification result is drawn. As shown in Table 4, the accuracy of each category has reached more than 80%, and the accuracies of the wetland, water, ice, snow, city, and tundra categories have reached more than 90%.  
Since the goal is to obtain a long time series of land cover classification results, there is also a high requirement for the generalization ability of the model over time. Therefore, 10% of the samples from the CNLUCC classification data from 1980, 1990, 1995, 2000, 2005, 2010 and 2015 is extracted for test of the accuracy of the model and the accuracy of each category for the seven years. Furthermore, the standard deviation of the accuracy of each category during these seven years is determined. The errors for each category do not exceed 6%, and the error of the overall accuracy is maintained at 0.8%.  
To further analyze the stability of the classification accuracy of the model, the maximum and minimum values and median of each category is also counted for different years and draws a box diagram (Figure 8). From this we can find that the accuracy of each category is kept at least 80%. The accuracy dispersion of a single category is relatively small. The accuracy of a single category is relatively small in time, and only three categories of forests, shrubs, and cities have outliers. However, the accuracy of these three outliers is greater than 80%, which is within an acceptable range. It is proven that the model also has good generalization for time series.