Traditionally, the in-situ investigation of landslides requires a large amount of manpower and is also very time-consuming. Presently, by applying spatial information (Geographic Information System; GIS and Remote Sensing; RS), we can obtain landform and land cover information systematically. Entropy-Based Classification is a typical classifier which renders relations among system inputs (condition attributes) and outputs (decision attributes). Also, the thresholds of inputs are attained. However, the accuracy of this classifier is determined by the selected data. Accordingly, through remote sensing of image data, we can quickly determine a landslide area by using an effective classifier. This study purposed Pixel-Based Classification (PBC) to extract landslide image information through GIS techniques. More specifically, the image data are used to present the current situation with different spectral and texture information to analyze the area surrounding the Wan-Da reservoir. The pixels with texture information are employed to compute the Entropy-based Classification. In this way, we expect to correlate the image attributes to the occur- rence/non-occurrence of the image data. The sensitivity area is analyzed and the relations among occurrence and non-occurrence are studied. The knowledge rules of images on landslides are rationally constructed.