# Coastal Zone Classification Based on U-Net and Remote Sensing
## 2 Experimental Methods
### Study Area and Experimental Environment
#### 2.1.2 Experimental Environment and Preprocessing  
Figure 2 presents the workflow for coastal zone classification using the U-Net deep learning framework and GF-2 remotely sensed data. The preprocessing of the GF-2 remote sensing images involved radiometric calibration, atmospheric correction, and cropping of the study area. The Apply Gain and Offset tool provided by ENVI(r)5.3 was utilized for radiometric calibration of the multispectral data. The absolute calibration coefficients for the GF-2 remote sensing images from 2017 are as follows: PAN channel (gain = 0.1503, bias = 0), B1 channel (gain = 0.1193, bias = 0), B2 channel (gain = 0.1530, bias = 0), B3 channel (gain = 0.1424, bias = 0), and B4 channel (gain = 0.1569, bias = 0). Based on field investigation and visual interpretation, the study area was classified into four categories: water body, artificial surface, forest land, and farm land. \"Forest land\" refers to areas dominated by trees, including both natural forests and plantations. \"Water body\" includes areas covered by water, such as rivers, lakes, wetlands, and artificial water bodies like reservoirs and ponds. \"Farm land\" encompasses land used for agricultural production, including arable land, orchards, vineyards, pastures, and agricultural facilities, primarily used for growing crops, raising livestock, and aquaculture. \"Artificial surface\" denotes areas where the natural land cover has been replaced by human-made constructions, including urban and rural buildings, roads, airports, industrial areas, and other infrastructure, typically covered by materials such as concrete, asphalt, and bricks. The corresponding training and testing label data were produced by remote sensing experts. Due to the large size of the full image, inputting it directly into the deep learning model could cause memory issues on the workstation. Therefore, the image was cropped into 256 \(\times\) 256-pixel slices, and 50 images were selected as sample data. The total number of pixels was 94,960, with a relatively balanced distribution across each category (as detailed in Table 1). The sample data were divided into an 8:2 ratio for training and validation.  
The experiments in this study were conducted on a Windows 10 operating system, using a hardware setup comprising an Intel Xeon(R) Gold 5118 processor, 128 GB of RAM, a 2 TB disk, and an RTX 2080 Ti 11 GB graphics card. The U-Net model was implemented using the MATLAB(r) R2020a programming environment. The training utilized the stochastic gradient descent with momentum (Sgdm) optimization method, set for 20 epochs, with each epoch consisting of 1000 iterations. The batch size was set to 16, the learning rate was fixed at 0.005, and the momentum was set at 0.9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Categories** & **Water Body** & **Artificial Surface** & **Forest Land** & **Farm Land** & **Total** \\ \hline Training samples & 16,658 & 20,518 & 21,779 & 17,013 & 75,968 \\ Testing samples & 4164 & 5130 & 5445 & 4253 & 18,992 \\ Total & 20,822 & 25,648 & 27,224 & 21,266 & 94,960 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected training and testing samples.