Taking the multiple features of remote sensing images into consideration, a new approach is presented to the classify remote sensing images based on the fusion of multiple features. The bag of visual words (BOVW) representation is firstly improved. Then, the BOVW feature, color histogram and Gabor texture feature are extracted from the images respectively. The classification is performed by the support vector machine classifier, and the final output is obtained through adaptively fusing the results by multiple features. The proposed method has been evaluated on a large publicly available remote sensing image dataset with 2 100 images. The experimental results have witnessed that the overall classification accuracy is boosted by 10% in comparison with the method based on the single feature which owns the highest accuracy. Comprehensive experimental results indicate that the proposed approach is effective and suitable for high-resolution remote sensing image classification.