## 3 Experiments and Results
### Results and Result Comparison  
In the comparative experiment, we applied our trained model to three GF-2 images for segmentation. These images were only used for testing and not involved in training. Figure 3 illustrates the results obtained from the comparison methods and proposed method. In Figure 3, the first columnillustrates the results of Experiment 1, the second column illustrates the results of Experiment 2 and the third column illustrates the results of Experiment 3. Tables 1-3 are confusion matrices c for the segmentation results of SegNet model, DeepLab model, and HSCNN model, respectively. Each row of the confusion matrix represents the proportion taken by the actual category, and each column represents the proportion taken by the predicted category. As can be seen from the tables, our method achieves better classification results. In the example above, the proportion of \"winter wheat\" wrongly categorized as \"background\" is on average 0.069, and the proportion of \"background\" wrongly classified as \"winter wheat\" is on average 0.019, resulting in an  
Figure 3: Segmentation results for Gaofen-2 (GF-2) images: (**a**) original images; (**b**) ground truth, (**c**) results of SegNet corresponding to the images in (**a**); (**d**) errors of SegNet; (**e**) results of DeepLab; (**f**) errors of DeepLab; (**g**) results of HSCNN; and (**h**) errors of HSCNN. Accuracy, precision, recall, and the Kappa coefficient were used to evaluate the models. These indices are calculated via mixing matrix c.  
Accuracy is the ratio of the number of correctly classified samples to the total number of samples, and is given in this case by the following equation:  
\[\text{Accuracy}\;=\;\frac{\sum_{i=1}^{2}\text{C}_{\text{ii}}}{\sum_{i=1}^{2} \sum_{j=1}^{2}\text{C}_{\text{ij}}} \tag{8}\]  
Here, \(\text{C}_{\text{ii}}\) denotes the number of correctly classified samples, and \(\text{C}_{\text{ij}}\) denotes the number of samples of class i misidentified as class j. Precision denotes the average proportion of pixels correctly classified to one class from the total retrieved pixels. Precision is calculated as:  
\[\text{Precision}\;=\;\frac{1}{2}\sum_{i}\text{C}_{\text{ii}}/\sum_{i}\text{C}_ {\text{ij}} \tag{9}\]  
Recall represents the average proportion of pixels that are correctly classified in relation to the actual total pixels of a given class. Recall is computed as:  
\[\text{Recall}\;=\;\frac{1}{2}\sum_{i}\text{C}_{\text{ii}}/\sum_{i}\text{C}_{ \text{ij}} \tag{10}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **GT/Predicted** & **Winter Wheat** & **Others** \\ \hline \multirow{2}{*}{Experiment-1} & winter wheat & 0.621 & 0.129 \\  & Others & 0.087 & 0.163 \\ \hline \multirow{2}{*}{Experiment-2} & winter wheat & 0.387 & 0.153 \\  & Others & 0.084 & 0.376 \\ \hline \multirow{2}{*}{Experiment-3} & winter wheat & 0.217 & 0.123 \\  & Others & 0.129 & 0.531 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Confusion matrix of the SegNet approach for Figure 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **GT/Predicted** & **Winter Wheat** & **Others** \\ \hline \multirow{2}{*}{Experiment-1} & winter wheat & 0.653 & 0.107 \\  & Others & 0.055 & 0.185 \\ \hline \multirow{2}{*}{Experiment-2} & winter wheat & 0.432 & 0.086 \\  & Others & 0.039 & 0.443 \\ \hline \multirow{2}{*}{Experiment-3} & winter wheat & 0.301 & 0.108 \\  & Others & 0.045 & 0.546 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix of the DeepLab approach for Figure 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **GT/Predicted** & **Winter Wheat** & **Others** \\ \hline \multirow{2}{*}{Experiment-1} & winter wheat & 0.681 & 0.075 \\  & Others & 0.027 & 0.217 \\ \hline \multirow{2}{*}{Experiment-2} & winter wheat & 0.458 & 0.062 \\  & Others & 0.013 & 0.467 \\ \hline \multirow{2}{*}{Experiment-3} & winter wheat & 0.329 & 0.071 \\  & Others & 0.017 & 0.583 \\ \hline \hline \end{tabular}
\end{table}