## 2 Materials and Methods
### Method
#### 2.3.3 LULC Classification System and Method  
Referring to the current land-use situation in the study area and the new version of the national standard classification of land-use status [37] organized and revised by the Ministry of land and resources of China in 2017, the land-use classes were divided into six categories, including five classes of unchanging land cover, namely, agricultural land, buildings, water, tidal flats, and vegetation. Additionally, one special class for land-change detection is called changed land. The specific detail of these six classes is described in Table 2.  
The classification strategy adopted is the pixel-based direct classification (DC) method. It is an effective method for processing time series data that such a scenario is not easy to model with expert knowledge but suitable for machine learning. Compared with the post-classification comparison method, it is more suited for LUCC because it can identify changes through only one stage without considering the error propagation from the wrong classification in each image [38]. The maximum-likelihood classification (MLC) based on statistical theory is a classification method used in the research; MLC is also called Bayesian classification. In two or more kinds of decisions of classification, this method uses a statistical method to set up a set of non-linear discriminant functions based on Bayesian Decision Criteria. Based on selecting training areas, assuming that each classification function corresponds to a normal distribution, the probability of the attribution of the area to be classified is calculated based on these statistics [39]. For this method, the results will \begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Main features** \\ \hline Agricultural land & Cultivated land, paddy field, orchard, greenhouse, fishpond \\ Building & Road, city, village, industrial area \\ Vegetation & Forest, grassland \\ Water & River, lake, stream \\ Tidal flat & Beach, river beach, lake beach \\ Changed land & Areas where the type of features has changed \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Land use/land cover feature information table. be used for accuracy analysis on the one hand and will be used as the basis of deep-learning sample editing on the other hand. Additionally, a deep-learning method of convolution neural network called U-Net was used in this study, which makes the input label images traverse the network with various weighting layers, including an input layer, an output layer, a convolution layer, and a pooling layer. The mapping rules between images and targets are established through various weight parameters. Many studies have confirmed the value and accuracy advantage of U-Net in the field of remote sensing change detection compared with traditional machine-learning classifiers such as Support Vector Machine (SVM) and Random Trees (RF) [40; 41; 42]. The U-Net model is a typical encoder-decoder architecture as an improvement and extension of FCN; its structure enables it to capture multiple features in the image context, which is advantageous for pixel-based DC method. It uses features spliced together in the channel size to form thicker features that are helpful for the analysis of time-series information. Meanwhile, U-Net can be trained from small datasets while ensuring reasonable segmentation accuracy. This is beneficial for change detection because there are relatively few training samples compared with other types of samples in study area [43; 44; 45]. Taking an image with 464 \(\times\) 464 pixels containing 12 bands as an input example, the specific architecture is divided into 27 convolution layers (Figure 3). As shown in it, the shrinking network in the left half of the figure consists of five levels. Each level uses 3 \(\times\) 3 convolution layer twice and 2 \(\times\) 2 maximum pooling for downsampling. The expansion network structure of the right part is opposite, but the procedure is similar. Cross-entropy was used as the loss function in our network.