## 3 Results and Analysis  
### Image Classification and Constraint Analysis for Landslide Extraction  
Supervised classification is a commonly used statistical method for assessing data. It involves training based on pre-existing information and then calculating weights to determine the classification of target features. Supervised classification requires that the selected training areas are typical and representative. The quality of the classification results is closely related to the selection of the training areas [26]. The region of interest is established as the prior model, which focuses on the landslide group near Nanyuan Township, Zhouqu County, Gansu Province. This area mainly comprises five types of land: vegetation, landslide, water, road, and building. Consequently, this study generated five training samples and independently selected multiple areas of interest for each terrestrial object category within the research area as training samples. Supervised classification methods mainly include the maximum likelihood method, the minimum distance method, support vector machine, the neural network classification method, the fuzzy classification method, and other approaches.  
This study primarily employs the maximum likelihood method to attain image classification results, as shown in Figure 4. As shown in Figure 4, the river and building segments in the upper left corner of the image have been categorized as landslides. Misclassification occurred due to erosion and burial resulting from the deposition of debris, which led to a compromised accuracy of the classification. The spectral and textural characteristics of the objects have been confused with information related to landslide features.  
Geomorphological type and slope are the primary factors that directly influence the occurrence of landslide hazards. As shown in Figure 5, the slope of the landslide area  
Figure 4: Classified landslide extraction result.  
is mainly concentrated within 15\({}^{\circ}\)-30\({}^{\circ}\), and the sidewall slope is greater than 40\({}^{\circ}\). The accuracy of identifying landslides through geomorphology is compromised by the burial of features caused by landslides. The recognition of landslides requires consideration of specific terrain factors to improve the classification of landslide areas. Thus, a constraint analysis was conducted using spatial analysis functions to combine slope factors based on the results of maximum likelihood classification.  
The overlay analysis is a commonly used spatial analysis method in Geographic Information Systems (GISs). It involves overlaying multiple spatial data layers to identify their spatial relationships and interactions. Overlay analysis was performed on the slope and supervised classification results. The slope was utilized to refine the supervised classification results based on a specific mathematical proportion, which eliminated errors caused by spectral characteristics in the supervised classification process. It also enabled accurate extraction of the landslide area.  
The results of the slope constraints are shown in Figure 6. As shown in Figure 6a, the classification results obtained by the maximum likelihood method are presented as binary classification maps to distinguish between landslide and non-landslide areas. Figure 6b,c illustrate the constrained extraction of landslides using the slope terrain factor from the DEM and compare the differences in observation results with the overlaid images in Figure 6a,b. The constrained extraction can be observed from the three black boxes located in the upper left corner of the image. By adding the terrain factor, the constraints of slope, rivers, buildings, and hills with excessive slopes in that area are eliminated. Through the application of weighted superposition of the slope, the majority of the rivers and buildings that were erroneously identified as a result of sediment erosion are effectively removed. As a result, the Jiangdingya landslide is further accurately extracted.  
Figure 5: Slope map.