## 3 Research Methods
### Feature Selection  
There are many kinds of image features. Choosing appropriate features can improve the accuracy and efficiency of object-oriented automatic classification. The principle of feature selection is to reduce the total quantity of data while not reducing the classification \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Type** & **Feature Name** & **Number of Features** \\ \hline \multirow{3}{*}{Spectrum} & Mean value of bands 1–4, Standard deviation of & 12 \\  & bands 1–4, Brightness, Max. diff, NDVI, NDWI & \\ \multirow{3}{*}{Geometry} & Area, Length, Width, Length/Width, Density, & 8 \\  & Compactness, Border length, Number of pixels & \\ \multirow{3}{*}{Texture} & Homogeneity, Contrast, Dissimilarity, Ang. 2 nd moment, Entropy, Correlation, StdDev, Mean & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature information. Figure 2: Technical flow chart. The maximum correlation and minimum redundancy are combined to form the mRMR algorithm, and the formula for calculating \(D\) and \(R\) using the operator \(\Phi(D,R)\) is shown in Equation (7) [17; 31]:  
\[max\Phi(D,R),\Phi=D-R \tag{7}\]  
Using this feature selection criterion, the features are selected by maximizing the defined operator \(\Phi()\), using an incremental search method. Based on the feature subset \(S_{k-1}\), the \(k^{\text{th}}\) feature is calculated from the remaining feature space \(X-S_{k-1}\), which is made to maximize \(\Phi()\) using the following equation, that is, the incremental feature selection optimization formula [17; 31]:  
\[\underset{x_{j}\in X-S_{k-1}}{max}\Bigg{[}I(x_{j};c)-\frac{1}{k-1}\sum_{x_{i} \in S_{k-1}}I(x_{j};x_{i})\Bigg{]} \tag{8}\]  
The weight of each feature is calculated according to the Fisher Score, and features with higher weight have better classification ability. As the correlation between features is not calculated, redundant features cannot be removed. However, the mRMR algorithm can obtain the feature subset that has the maximum correlation with the target category and the least redundancy, but it cannot obtain the weight coefficient of each feature, and the extracted feature subset cannot reflect the difference of the effect of different features on the classification. Firstly, the Fisher Score calculation method was used to build the ranking rules of feature index importance, and the features with larger weight were selected by calculating the weight value of each feature. The feature vector with a high weight can be used as the dominant vector of the classification set, and the feature vector with a low weight has less influence on the classification result. Then, the mRMR algorithm was used to calculate the selected features, and the features with the maximum correlation and the minimum redundancy between the categories were selected. Therefore, by combining the Fisher Score and mRMR algorithms for feature dimension reduction, an optimal feature subset can be obtained. In addition to the above two methods, in order to verify the reliability in the experiment, the commonly used recursive feature elimination (RFE) algorithm (a wrapped feature selection method) and logistic regression (LR) algorithm (an embedded feature selection method) were selected. (3) RFE is a greedy algorithm [35]. It takes the whole data set as the starting point of the search and uses a feature ordering approach to select backward sequences from the whole set, eliminating one feature with the lowest ranking each time, until the feature subset that is most important for the classification results is selected. In the iterative process of the above steps, the order in which features are eliminated depends on their importance. The RFE algorithm requires a suitable classifier for modeling and prediction, for which the linear regression model was used in our experiment. (4) LR is a machine learning model with simple form and good interpretability [36]. The LR model studies the multiple regression relationship formed between one dependent variable and multiple independent variables.