The integration of multi-source remote sensing images and multi-feature parameters is effective in the accurate identification of target ground objects. However, excess feature parameters can cause data redundancy, reducing classification accuracy. Focusing on a sugarcane planting area with Karst landforms, this study extracted the spectral, index, texture, topographic, and polarization features of the ground objects in the study area from Sentinel-1/2 images and SRTM digital elevation data. The index features involved the red edge index calculated based on the red-edge band, which was scarce in data derived from remote sensing sensors, and the texture features included the Radar image textures. In the experiment, six schemes were designed to explore the effects of different image features and the random forest-based optimal feature association on sugarcane information extraction. The results show that for the classification of ground objects in the study area using spectral features combined with other feature types, the importance of the feature types ranked in descending order of spectral features, index features, texture features, topographic features, and polarization features. Among the six schemes, the scheme based on the random forest algorithm, integrating different feature variables, yielded the optimal information extraction effect for sugarcane, with both user and producer accuracy higher than 97%, overall accuracy of 95.49%, and a Kappa coefficient of 0.94.