Abstract: Alpine wetlands, a critical part of the natural ecosystem in the Qinghai-Tibet Plateau, serve as extremely significant water conservation and climate regulation areas in China. Accurately extracting land cover information of alpine wetlands is crucial for local ecological security monitoring and protection. This study performed object-oriented classification of the data from the Zoige wetland, including the Zhuhai-1 hyperspectral remote sensing image, Sentinel-2A remote sensing image, and Landsat-8 OLI image, integrated with spectral, textural, and topographic features. The results show that the overall data classification accuracy of the three images exceeded 85 %, with a Kappa coefficient above 68 %. The optimal classification result was observed in the Zhuhai-1 hyperspectral remote sensing image. The three images showed generally consistent data classification results, with marsh wetlands being the dominant land type. They indicated roughly the same distribution of riverine and lacustrine wetlands and slightly varying distributions of alpine grasslands, with minor area differences. Additionally, they displayed minimally different distributions of desertified land and almost the same hydrographic net distribution except for slightly different tributary distributions. This study fully explores the combinations of spectral features favorable for image classification, improving the identification accuracy of remote sensing images and providing technical support for the conservation of alpine wetlands.