## 4 Object-Oriented Classification Process
### Feature Selection Results  
As the classification results of image classification are influenced by the number of samples and spatial location, stratified random sampling was adopted for each category of features, such that the number of samples in each category was proportional to the total area of the category. We selected 2/3 of the segmented objects to extract the features,  
Figure 3: Typical ground object segmentation plot: (**a**) buildings; (**b**) water; (**c**) roads; (**d**) bare land. including texture, geometric, and spectral feature values, while the remaining samples were used for accuracy testing. To explore the importance ranking of the relevant features, the top 15 features obtained with the five feature selection methods are listed in Table 2, while the proportions of different types of features in different subsets are shown in Table 3. The results show that the features screened by different feature selection methods presented significant differences. In general, spectral and texture features accounted for a large proportion of the top 15 features. Figure 4 shows the correlation coefficient matrix of the top 15 features of different feature selection methods. The darker the grid color, the smaller the correlation coefficient between the features, and the more negative the correlation between the two features. On the contrary, the larger the correlation coefficient between features, the more positive the correlation between the two features. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fm** & **Fisher** & **mRMR** & **REF** & **LR** \\ \hline Standard\_G & NDVI & Standard\_R & GLCM\_Entropy & GLCM\_Ang\_2 nd \\  & & & & moment \\ Density & NDWI & NDVI & Compactness & GLCM\_Correlation \\ Standard\_R & Mean\_NIR & Length/Width & Standard\_B & NDWI \\ Mean\_B & Mean\_B & GLCM\_StDev & Standard\_R & Width \\ Width\_Pxl & Area\_Pxl & Standard\_G & LengthWidth & GLCM\_Mean \\ Border\_length & Standard\_B & Density & GLCM\_StdDev & length \\ NDVI & Standard\_NIR & Compactness & GLCM\_Dissimilarity & GLDV\_Entropy \\ Max\_diff & Max\_diff & GLCM\_Correlation & GLCM\_Mean\_ & GLCM\_Homogeneity \\ GLDV\_Entropy & Mean\_R & GLCM\_Dissimilarity & Mean\_B & GLCM\_StdDev \\ Standard\_B & Width & Mean\_B & Mean\_G & Density \\ NDWI & Mean\_G & Width\_Pxl & Mean\_NIR & max\_diff \\ Mean\_NIR & GLCM\_Homogeneity & NDWI & Mean\_R & Standard\_NIR \\ GLDV\_Ang\_2 nd moment & Standard\_G & Number\_of\_ & Standard\_G & Length/Width \\ Mean\_R & GLCM\_Ang\_2 nd & Standard\_B & GLDV\_Entropy & NDVI \\ GLCM\_Homogeneity & & & GLCM\_Mean & Standard\_NIR & Standard\_G \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top 15 features using various FS methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Selection** & **Feature Description** & **Spectral** & **Geometric** & **Texture** \\ \hline Fm & Number of features & 9 & 3 & 3 \\ Top 15 feature ratios & 60.00\% & 20.00\% & 20.00\% \\ Fisher & Number of features & 11 & 2 & 2 \\ Top 16 feature ratios & 73.33\% & 13.33\% & 13.33\% \\  & Number of features & 7 & 5 & 4 \\ mRMR & Top 17 feature ratios & 46.67\% & 33.33\% & 2  
Figure 4: The correlation matrix of the top 15 features of the five feature selection methods: (**a**) Fm; (**b**) Fisher; (**c**) mRMR; (**d**) RFE; (**e**) LR. Overall, the spectral features appeared significantly more frequently than the geometric and texture features. In the filtered feature selection method, NDVI, NDWI, Mean_B, Standard_B, Standard_G, and Width features were all present, while the number of texture features was more than 1/3. The mRMR and RFE algorithms selected seven features among the first 15 features which were the same, showing strong consistency. In addition, the LR algorithm appeared to choose more geometric and texture features, reaching 50% of the features in each category. From the feature extraction results, it can be seen that spectral features were the most numerous.