# Coastal Zone Classification Based on U-Net and Remote Sensing
## 2 Experimental Methods
### Image Feature Extraction  
High-resolution remote sensing images contain a wealth of information beyond conventional spectral data, including rich geometric, spatial, and texture information. This complexity arises because different ground objects can exhibit similar spectral characteristics, such as buildings and ground surfaces, while identical objects can display varied spectral information, like roofs of buildings made from different materials. Additionally, GF-2 images are limited to three visible light bands and one near-infrared band, providing insufficient spectral information. Due to these characteristics, relying solely on spectral data for training deep learning models often yields suboptimal results, as the models may not adequately capture the diverse feature information present in the images. Therefore, it is essential to construct a variety of image features to complement the original spectral data and enhance classification accuracy. In this study, we constructed and integrated image texture features, contrast features, and vegetation features as supplements to the original spectral data. These features were combined and fed into the deep learning network, specifically the U-Net model, to provide more comprehensive inputs. By focusing on the unique characteristics of the U-Net model, this research aimed to utilize a fusion of three-category feature information with spectral data, offering the model more robust and sufficient inputs for accurate classification. Focusing on the characteristics of the U-Net deep learning model, this research constructed three types of feature information and integrated them with spectral information to provide the model with more comprehensive inputs. (1) Texture features extracted with GLCM. GLCM texture feature extraction is based on a statistics algorithm. In this research, five texture features were extracted, including contrast (CON), correlation (COR), angular second moment (ASM), mean, and entropy (ENT), using GF-2 RGB channels with four angle directions (0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\)). The CON measures the local variations in the GLCM matrix, reflecting image clarity and texture depth. The calculation formula of CON is described in Equation (1) as follows:  
\[CON=\sum\
olimits_{\mathrm{i}\mathrm{j}}(\mathrm{i}-\mathrm{j})^{2}\mathrm{P}( \mathrm{i},\mathrm{j}) \tag{1}\]  
where \(\mathrm{i}\) and \(\mathrm{j}\) mean the row and column indices in the GLCM, and \(\mathrm{P}(\mathrm{i},\mathrm{j})\) means the normalized frequency of the co-occurrence of gray levels \(\mathrm{i}\) and \(\mathrm{j}\) in the GLCM. Figure 3: Structure of U-Net network. The COR measures the degree to which a pixel is correlated with its neighbors across the entire image, reflecting the correlation of local grayscale values. The calculation formula for COR can be described with the following Equation (2):  
\[COR=\sum\
olimits_{i}\mathrm{j}\frac{(i-\mu_{i})(j-\mu_{j})P(i,j)}{\sigma_{i} \sigma_{j}} \tag{2}\]  
where i and j mean the row and column indices in the GLCM, \(P(i,j)\) means the normalized frequency of the co-occurrence of gray levels i and j in the GLCM. \(\mu\_i\) is the mean gray level of row i in the GLCM, \(\mu\_j\) is the mean gray level of column j in the GLCM, \(\delta_{i}\) and \(\delta\_j\) are the standard deviations of the gray levels in row i and column j, respectively. The ASM, also known as energy, measures the uniformity or texture uniformity of the image. It reflects the uniformity of the grayscale distribution and the texture's fineness. The calculation formula for ASM can be described with Equation (3), as follows:  
\[ASM=\sum\
olimits_{i}\mathrm{j}P(i,j)^{2} \tag{3}\]  
where i and j mean the row and column indices in the GLCM, and \(P(i,j)\) means the normalized frequency of the co-occurrence of gray levels i and j in the GLCM. Mean represents the average value of the intensities in the GLCM, indicating the overall brightness of the image tone.