# Coastal Zone Classification Based on U-Net and Remote Sensing
## 2 Experimental Methods
### Accuracy Assessment  
In remote sensing classification tasks, accurately evaluating the performance of classification algorithms is crucial. Commonly used metrics for this purpose include overall accuracy (\(OA\)), the Kappa coefficient, and the F1-score [71; 72]. In this study, these metrics were selected for quantitative evaluation of classification results [73; 74]. \(OA\) was chosen because (1) \(OA\) is straightforward to understand and calculate; (2) it provides a quick overview of the classifier's performance; (3) it is a standard metric in many fields, making results easily comparable across studies. The Kappa coefficient was selected because (1) it accounts for the agreement occurring by chance; (2) it is less sensitive to class imbalance; (3) Kappa values closer to 1 indicate better agreement than expected by chance, while values below 0 indicate worse than random performance. The F1-score was included because (1) it provides a single metric that balances both false positives and false negatives; (2) it is useful in cases of class imbalance by considering both precision and recall; (3) it offers detailed insights into the classifier's performance.  
_OA_ represents the ratio of correctly classified samples to the total number of samples. The calculation formula for OA is shown in Equation (11), as follows:  
\[OA=\frac{TP+TN}{TP+TN+FN+FP} \tag{11}\]  
where _TP_ (true positive) represents the number of positive samples that are classified correctly, _TN_ (true negative) represents the number of negative samples that are classified correctly, _FP_ (false positive) represents the number of positive samples that are classified incorrectly, and _FN_ (false negative) represents the number of negative samples classified incorrect.  
The _Kappa_ coefficient is a classification evaluation index based on the confusion matrix, with values ranging from \(-1\) to 1, although in practical applications it typically fallsbetween 0 and 1. The _Kappa_ coefficient reflects the overall classification accuracy of a given model, with higher values indicating greater accuracy. The calculation formula for the _Kappa_ coefficient is shown in Equation (12), as follows:  
\[Kappa=\frac{N\times\sum\limits_{i=1}^{p}a_{ii}-\sum\left(a_{i+}\times a_{+i} \right)}{N^{2}-\sum\left(a_{i+}\times a_{+i}\right)} \tag{12}\]  
where \(p\) represents the row or column of a confusion matrix, \(a_{ii}\) represents the number of correctly classified, which corresponding to the value of the \(i\)-th row and \(i\)-th column on the diagonal, \(a_{+i}\) represents the sum of the \(i\)-th row, \(a_{i+}\) represents the sum of the \(i\)-th column, and \(N\) is total number of samples.  
The _F1-score_ is the harmonic mean of precision (\(P\)) and recall (\(R\)). It is a comprehensive indicator that reflects the model's ability to distinguish between positive and negative samples. The calculation formula for the _F1-score_ is shown in Equation (13), as follows:  
\[F1=2\times\frac{P\times R}{P+R} \tag{13}\]  
where \(P=\frac{TP}{TP+FP}\), \(R=\frac{TP}{TP+FN}\), \(TP\), \(FP\), and \(FN\) are defined the same as in Formula (11).