Given the complex and variable distribution of high-resolution remote sensing images and the fast classification performance of the extreme learning machine (ELM), we propose a multi-feature multi-kernel high-resolution remote sensing image classification method based on ELM. Firstly, the original image is roughly divided into several feature regions by the multi-scale segmentation algorithm. Then the object information of typical earth features is obtained by merging the coarse segmentation images according to the region merging criterion, and the spectral features and spatial features of the segmentation objects are extracted. A multi-kernel ELM via weighted combination of kernel functions is used to classify images, and the final classification results are obtained. Experimental results show that the proposed method not only reduces the requirements for the target training samples, but also improves the accuracy, timeliness and integrity of the classification.