The Yulong porphyry copper belt in Xizang, located in the eastern part of the Tethys-Himalaya metallogenic belt, is a globally renowned copper mineralization prospect area. Despite the challenging natural conditions in the mining area, which pose difficulties for traditional surface exploration work, the sparse vegetation facilitates the extraction of remote sensing information. In this study, we used ASTER remote sensing imagery as the data source and applied principal component analysis (PCA) and imaging spectrometry to extract alteration and mineralization information from the Yulong porphyry copper belt. Specifically, PCA was used to extract information on iron staining alteration anomalies using ASTER bands 1～4, carbonate alteration anomalies using bands 1, 3, 4, and 5, Mg-hydroxyl alteration anomalies using bands 1, 3, 4, and 8, and Al-hydroxyl alteration anomalies using bands 1, 3, 6, and 7. Imaging spectrometry, on the other hand, involved techniques such as minimum noise fraction (MNF), pure pixel index (PPI), n-dimensional visualizer (n-D Visualizer), and mixture tuned matched filtering (MTMF) to map the distribution of alteration mineral information in the Yulong porphyry copper belt. By comprehensively analyzing the results obtained from both methods and considering geological factors such as stratum and tectonics, we constructed a remote sensing mineral exploration model and predicted four mineralization prospect areas. Finally, through comparison and verification with known mineral occurrences, we confirmed that the extracted mineralization alteration areas closely matched the actual mineral occurrences, demonstrating the effectiveness and reliability of these two methods. This study not only provides strong technical support for large-scale exploration in the Yulong porphyry copper belt but also lays a solid foundation for the precise delineation of future target areas.