## 4 Results and Analysis
### Analysis of Apple Orchard Extraction Accuracy for Different Feature Combinations  
To further verify the apple orchard extraction accuracy of different feature combinations, the RF classification accuracy of different classification feature combinations was analyzed, and the accuracy of different classification feature combinations is shown in Table 4.  
Analysis of Table 4 shows that:  
Comparing the accuracy of feature combinations G1 to G4, G1 had an OA of 80.56%, an F1 score of 83.64%, and a kappa coefficient of 66.95%. G2 had an OA of 70.83%, an F1 score of 75%, and a kappa coefficient of 52.65%. G3 had an OA of 56.67%, an F1 score of 25.88%, and a kappa coefficient of 29.18%. G4 had an OA of 67.78%, an F1 score of 58.12%, and a kappa coefficient of 45.29%. All classification accuracies for G1 were higher than those for G2, G3, and G4, indicating that the overall classification of RGB Bands in the study area in October 2022 was better, whereas using only the improved spectral angle features or texture features distinguishing all land use types in the study area was less effective and had lower OA and F1 scores.  
Comparing the classification accuracies of feature combinations G5 to G8, the OA and F1 scores of G5, G6, and G8 were greater than 80% when the RGB Bands were combined with other features, achieving a relatively good classification result. Although the results of G4 (improved spectral angle features) were not good when classified alone, the OA, kappa coefficient, and F1 score of G8 (RGB Bands + improved spectral angle features) were the best  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Combination of Features** & _OA/\%_ & _Kappa_ & _UAI/\%_ & _PA/\%_ & **F1 _Scoret/\%_** \\ \hline G1 & 80.56 & 66.95 & 93.88 & 75.41 & 83.64 \\ G2 & 70.83 & 52.65 & 76.27 & 73.77 & 75.00 \\ G3 & 56.67 & 29.18 & 45.83 & 18.03 & 25.88 \\ G4 & 67.78 & 45.29 & 60.71 & 55.74 & 58.12 \\ G5 & 80.56 & 66.41 & 88.68 & 77.05 & 82.46 \\ G6 & 80.56 & 66.63 & 88.89 & 78.69 & 83.48 \\ G7 & 78.06 & 63.61 & 91.84 & 73.77 & 81.82 \\ G8 & 80.83 & 67.17 & 92.16 & 77.05 & 83.93 \\ G9 & 79.44 & 66.33 & 88.68 & 77.05 & 82.46 \\ G10 & 83.06 & 70.70 & 92.45 & 80.33 & 85.96 \\ G11 & 82.78 & 70.55 & 92.73 & 83.61 & 87.93 \\ G12 & 84.72 & 74.69 & 92.86 & 85.25 & 88.89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of random forest classification using different features.  
for the combination of features G5-G8, reaching 80.83%, 67.17%, and 83.93%, respectively, indicating that the combination of RGB Bands with improved spectral angle features can effectively improve the separability of the feature space, which in turn improves the classification accuracy and is more suitable for apple orchard extraction in the Zhaotong City area.  
Comparing the classification accuracies of feature combinations G9 to G12, we observed that G10 outperformed G9 in terms of OA, kappa coefficient, and F1 score, indicating that the improved spectral angle feature was more effective than NDVI in identifying apple orchards. The OA, kappa coefficient, and F1 score of G12's feature combinations were the highest at 84.72%, 74.69%, and 88.89%, respectively. Among the 12 feature combinations, G11 had a slightly lower OA, kappa coefficient, and F1 score, indicating that the addition of texture features could improve the separability of features in the study area to a certain extent and that the combination of RGB Bands, NDVI, texture features, GLCM, and improved spectral angle features could effectively identify apple orchards.