## 3 Results
### Confusion Matrix Analysis  
According to the validation samples of raster data, which have been described before, Table 3 presents the confusion matrix results of each classification strategy. It shows that time-series NDVI is better for LULC classification than other types of NDVI data, not only in the overall accuracy but also in any class of land cover. Because the verification data has no intersection with the U-Net training or testing data, the information of these parcels has never been exposed to network training that can use them to test the robustness of the method. The accuracy results illustrate that the robustness of U-Net is better than MLC. In addition, after adding the sample of changed land in MLC, there are a lot of areas incorrectly identified as changed land that appear in these validation samples, which leads to a decrease in accuracy. A total of 61,633 pixels, which only account for 1.16% of the total, were classified as changed land by the U-Net classifier, while the MLC was up to 818,515, which accounts for 15.43% of the total. Various precision indexes of five and six classification types based on MLC also show that the expansion of the classification system will lead to a decline in the overall accuracy.  
When using point validation data, the overall accuracy of MLC and U-Net is 79.38% and 85.26%, respectively, in the confusion matrix results (Figure 10a,b). This also proves that the U-Net method has higher precision than the MLC method. For changed land, although TP accounts for more than U-Net in the MLC method, the problem of extracting too much changed land still exists. The calculated F1-scores of MLC and U-Net are 0.77 and 0.66, respectively. It is considered that U-Net is more reasonable for the classification of changed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data** & **Method** & **Kappa** & \begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} & **Class** & **Recall** & **Precision** & **F1-Score** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Single \\ NDVI \\ \end{tabular} } & \multirow{4}{*}{MLC} & \multirow{4}{*}{0.36} & \multirow{4}{*}{52.32} & Agricultural land & 46.62 & 61.46 & 0.53 \\  & & & Vegetation & 56.79 & 77.28 & 0.65 \\  & & & Water & 62.12 & 86.67 & 0.72 \\  & & & Buildings & 54.15 & 45.91 & 0.50 \\  & & & Tidal flats & 39.65 & 0.51 & 0.01 \\ \hline \multirow{4}{*}{\begin{tabular}{c} Mean \\ NDVI \\ \end{tabular} } & \multirow{4}{*}{MLC} & \multirow{4}{*}{0.45} & \multirow{4}{*}{62.15} & Agricultural land & 67.31 & 65.87 & 0.67 \\  & & & Vegetation & 48.16 & 91.60 & 0.63 \\  & & & Water & 44.84 & 99.25 & 0.62 \\  & & & Buildings & 71.01 & 52.11 & 0.60 \\  & & & Tidal flats & 52.15 & 2.02 & 0.04 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Time-series \\ NDVI \\ \end{tabular} } & \multirow{4}{*}{MLC} & \multirow{4}{*}{0.71} & \multirow{4}{*}{80.40} & Agricultural land & 85.98 & 77.11 & 0.81 \\  & & & Vegetation & 56.49 & 90.98 & 0.70 \\  & & & Water & 71.73 & 89.92 & 0.80 \\  & & & Buildings & 91.00 & 78.86 & 0.84 \\  & & & Tidal flats & 34.64 & 64.44 & 0.45 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} U-Net \\ \end{tabular} } & \multirow{4}{*}{MLC} & \multirow{4}{*}{0.59} & \multirow{4}{*}{69.65} & Agricultural land & 62.65 & 78.15 & 0.70 \\  & & & Vegetation & 56.50 & 90.97 & 0.70 \\  & & & Water & 68.84 & 95.02 & 0.80 \\  & & & Buildings & 90.10 & 80.35 & 0.85 \\  & & & Tidal flats & 34.89 & 64.50 & 0.45 \\  & & & Changed land * & — & \\ \hline \hline \end{tabular}  
* Indicates that these data lack samples in the ground truth. However, this class exists in the result of the classifier, which causes the denominator to be 0 during calculation and leads to meaningless data.  
\end{table}
Table 3: Accuracy evaluation of confusion matrix.  
land. One problem with U-net is that the classification effect of small samples, such as tidal flats (training sample pixels account for less than 0.1% of the total input images), is poor.