# Coastal Zone Classification Based on U-Net and Remote Sensing
## 3 Results and Analysis
### Classification with Multi-Features  
To illustrate the impact of multi-feature classification, we present the results for the original image classification, the original image combined with texture features, NDVI features, contrast features, and all features combined (Figure 6). The averaged OA, Kappa coefficients, and F1-score for each class achieved using the U-Net model with these multi-features are shown in Table 3.  
As seen in Table 3, compared to the original image classification results, the accuracy for forest land classification increased by 11% with the addition of NDVI features. This improvement highlights the significant impact of NDVI features on the accuracy of vegetation category classification. When contrast features were combined with the original images, the classification accuracy for forest land decreased slightly by 0.04, but this reduction was minor and did not significantly affect the overall accuracy. Notably, the classification accuracy for water bodies and cultivated land improved substantially (both increasing by 0.11), indicating that the addition of contrast features enhances the distinction between water bodies, cultivated land, and other ground objects. The inclusion of texture features with the original image improved the classification accuracy across all categories, demonstrating the strong influence of texture features in the classification process. As shown in Table 3 and Figure 7, the highest overall classification accuracy (OA of 92.65% and Kappa coefficient of 0.89) was achieved by combining texture, vegetation, and contrast features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **U-Net** & **SegNet** & **DeepLab v3+** & **SVM** & **RF** \\ \hline OA/\% & 86.32 & 85.48 & 86.12 & 82.58 & 84.73 \\ Kappa & 0.84 & 0.81 & 0.82 & 0.76 & 0.78 \\ F1-score & 0.85 & 0.84 & 0.84 & 0.81 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Averaged OA, Kappa, and F1-score of selected models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Original Image** & **+NDVI** & **+Texture** & **+Contrast** & **+Multi-Feature** \\ \hline Artificial surface & 0.89 & 0.92 & 0.92 & 0.9 & 0.94 \\ Wood land & 0.86 & 0.97 & 0.91 & 0.82 & 0.97 \\ Farm land & 0.65 & 0.68 & 0.71 & 0.76 & 0.88 \\ Water body & 0.87 & 0.88 & 0.95 & 0.98 & 0.98 \\ OA/\% & 86.32 & 88.95 & 89.74 & 87.93 & 93.65 \\ Kappa & 0.84 & 0.85 & 0.86 & 0.85 & 0.89 \\ F1-score & 0.85 & 0.89 & 0.89 & 0.87 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Averaged accuracy value per land cover class achieved with multi-feature using U-Net classifier.  
Figure 6: Multi-feature image classification results based on U-Net. (**a**) Original RGB image. (**b**) Original RGB image classification results. (**c**) Original image + texture feature classification result. (**d**) Original image + NDVI classification result. (**e**) Original image + contrast feature classification result. (**f**) Original image + multi-feature (texture, vegetation, contrast) classification result.  
In summary, the U-Net model that integrates texture, NDVI, and contrast features achieves superior classification results. This model fully leverages the information from high-resolution remote sensing images, making it particularly well-suited for coastal LULC classification.