In this paper, a novel super resolution (SR) method for remote sensing images based on compressive sensing (CS), structure similarity and dictionary learning is proposed. The basic idea is to find a dictionary which can represent the high resolution (HR) image patches in a sparse way. The extra information coming from the similar structures which often exist in remote sensing images can be learned into the dictionary, so we can get the reconstructed HR image through the dictionary in the CS frame due to the redundance in the image which has a sparse form in the dictionary. We use K-SVD algorithm to find the dictionary and OMP method to reveal the sparse coding coefficient's location and value. The difference between our method and the previous sample-based SR method is that we only use low-resolution image and the interpolation image from itself rather than other HR images. Experiments on both optical and laser remote sensing images show that our method is better than the original CS-based method in terms of not only the effect but also the running time.© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.