## 1 Introduction
### Image Classification by Means of Machine Learning  
A decision tree (DT) is one of the most intuitively effective classifiers applied in the automatic identification of remote-sensing images. CART is one of the DT classification algorithms, and it is widely used in machine learning and artificial intelligence [14; 15; 16]. CART is an intelligent algorithm that uses recursive segmentation technology to build a prediction model, which analyzes the relationship between multiple attributes and decisions to generate easy-to-understand rules for prediction [15; 16; 17]. The CART method is widely used in remote-sensing classification-based tasks, such as land cover mapping, forest remote-sensing surveys, crop area extraction, land desertification mapping, etc. [18; 19]. The Gini coefficient is engaged in the measurement of the impurity of a given element with respect to the rest of the classes in CART [20; 21]. Accordingly, by using a set of features, the maximum depth of the decision tree is reached.  
The DT can be modeled as a set of if-then rules, which is applicable in categorical data. Once the model has been developed, classification is extremely quick because no further complex mathematics is required. Problems with DTs include the possibility of generating a nonoptimal solution and overfitting. The latter is normally addressed by pruning the tree, removing one or more layers of splits. Pruning reduces the accuracy of classifying the training data but generally increases the accuracy of dealing with unknowns [22].  
In this study, a DT-based land cover classification was proposed by using Gaofen series satellite images, taking Yuli County, Xinjiang, China, as the study area. First of all, GF-2/GF-3 data were introduced, and a preprocessing procedure was demonstrated. Next, various features of optical and SAR images were investigated, and a multiseasonal merged SAR image was displayed. In addition, the CART algorithm of the decision tree was applied to train the classifier and to output the classification result. Finally, a case study of the DT-based classification was presented for Dunkuotan Village of Yuli County, and a classification accuracy assessment was proposed. The supervised classification scheme  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **Date of Launch** & **Sensors** & **Missions** \\ \hline \multirow{4}{*}{GF-1} & \multirow{4}{*}{26 April 2013} & 2 panchromatic multispectral cameras (2 m for panchromatic, 8 m for multispectral); 4 multispectral wide-width cameras (16 m) & Land resources and agricultural meteorology \\  & & & (16 m) & \\  & & 2 panchromatic multispectral & Land and resources, urban management, \\  & & & 4 m for multispectral) & Marine application, \\  & & & & disaster processing, water \\  & & & & protection and \\  & & & & management, meteorology \\  & & & & prevention, mitigation of \\  & & & & emergent disasters \\  & & & & Remote sensing of disaster \\  & & & & reduction, forestry, \\  & & & & meteorology \\  & & & & Monitoring atmospheric \\  & & & aerosol, sulfur dioxide, \\  & & & & nitrogen dioxide, methane, \\  & & & water quality, straw \\  & & & & burning, urban heat island \\  & & & & Land resources, \\  & & & & agricultural meteorology, \\  & & & & identification of \\  & & & & ground crops \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table}
Table 1: The payload and mission of the Gaofen satellite constellation of China.  
proposed in this paper could effectively promote the routine administrative and supervised workflow when remote-sensing images are applied, according to our investigation.