## 3 Research Methods
### Image Classification  
In the classification process, the choice of classifier is an important factor determining the classification results. The CART decision tree, RF, \(k\)-nearest neighbors (\(k\)NN), and support vector machine (SVM) methods comprise four different classification algorithms.  
The basic principle of CART is to form the test variables and the target variables into a data set, select the optimal segmentation features by calculating the Gini coefficient, then build a binary tree according to the feature values. These steps are cycled until the sample set to be classified reaches a stopping condition. There are two conditions for stopping: One is that there are no more feature variables for the target classification, and the other is that all samples of a given node belong to the same class. If the sample points of the stop classification node are of multiple classes, the node is specified as the class with the highest number of subclasses, and a new leaf is created within that class [37]. The binomial tree structure of the CART decision tree greatly improves the operational efficiency compared to the multinomial tree structure of the traditional decision tree [38].  
The RF algorithm is an integrated classifier based on multiple decision trees. Through a bootstrap sampling method, a subset of samples is randomly selected from the original data as training samples, and decision trees are constructed for each training sample separately. A randomly selected feature is used as a node (\(m<N\)) of the decision tree, which is split and grown based on the amount of feature information. The training process is iterated until the maximum tree depth set by the user is reached or the splitting cannot continue [39]. An RF consists of \(N\) decision trees, and voting is used for each decision tree to obtain the final classification result [40]. RF has the advantage of high prediction accuracy, coupled with the fact that it is less prone to overfitting. Therefore, it has been widely used for image classification in high-resolution remote sensing data sets.  
The \(k\)NN classification algorithm is a relatively simple machine learning algorithm [41]. In remote sensing image classification, this method determines the nearest \(k\) neighbors by calculating the distance between the samples to be classified and the training samples, then judges according to the categories of these \(k\) neighbors selected. The category to which the \(k\) neighbors belong the most is selected, and the samples to be classified are considered to belong to this category.  
The SVM is a new machine learning method developed on the basis of statistical learning theory [42; 43]. It is a non-parametric classifier. Based on the structural risk minimization criterion, the SVM solves image classification and regression problems by finding the optimal classification hyperplane in the high-dimensional feature space. According to the limited sample information, the best compromise between learning accuracy and learning effect can be obtained. The support vector machine has the advantages of simple implementation and high operational efficiency.  
The above four classification algorithms each have their own advantages. In this study, all four algorithms are used to classify the optimized feature combination and the unoptimized full feature combination.