D. BoW Model and SVM

Li et al. [32] first introduced the image method based on the BoW model. They believed that an image can be analogized to a document and the "words" of an image can be defined as feature vectors. The basic BoW model regards an image as a set of feature vectors and statistics of occurrence frequency of feature vectors, which are used for terrain classification. The BoW model can be set up by a clustering algorithm that is used to obtain the visual dictionary and the steps are as follows. Feature extraction: *m* images (*m* ≥ 50) are collected for each terrain type and each image is extracted by SURF-BRISK to obtain *n*(*i*) feature vectors. All terrain images form a total sum (*n*(*i*)) of feature vectors (words). Generation of dictionary/codebook: The feature vectors obtained from the previous step are clustered (here, the *k*-means clustering method is used [33]) to get *k* clustering centers in order to build

the codebook. A histogram is generated according to the codebook. The nearest neighbor calculation of each word of the picture is used to find the corresponding words in the codebook in order to form the BoW model.

SVM is an excellent learning algorithm developed on the basis of statistics theory and is widely used in many fields, such as image classification, handwriting recognition, and bioinformatics. The input vector is mapped to a high-dimensional feature space by nonlinear mapping (a kernel function) and an optimal hyperplane is constructed in this space. Compared with the artificial neural network, which suffers from an overfitting problem, the support vector machine has better generalization ability for unknown samples [34]. SVM can be divided into three groups: linear separable, nonlinear separable, and kernel function mapping. Linear classifier performance is limited to linear problems, because in nonlinear problems constraints of excessive relaxation can lead to a large number of error samples. At this point, it can be transformed into a linear problem in a high-dimensional space using nonlinear transformation in order to obtain an optimal classification hyperplane.
