The altitude information extracted from a single remote sensing image can be applied to detect the natural disaster, such as landslide or mud-rock flow. An approach based on dark channel prior was proposed for the altitude extraction from a single remote sensing image, which considers the influence of shadow. The approach was based on dark channel prior, and meanwhile a solution to overcome the effect of mountain shadow was given. The quantitative and qualitative analysis of a large number of mountain remote sensing images demonstrates that the proposed algorithm can obtain the accurate relative altitude information. In conclusion, the improved algorithm is effective for the extraction of the relative altitude from single remote sensing image of mountain with shadows.

To solve the problem of spectral distortion in remote sensing image fusion, an improved transform method based on midway image equalization was proposed. First, the multispectral image was decomposed by IHS (Intensity, Hue, Saturation) transform; then by using the midway image equalization, the cumulative histogram of panchromatic image and the spectral intensity component of multispectral image were adjusted to be the same; finally, the inverse transform of IHS was implemented and a high quality fusion image was obtained. The theoretical analysis and experimental results show that the proposed algorithm can not only suppress the spectral distortion of the fusion image, but also preserve the spatial resolution of the fusion image effectively, and it is simple and easy to implement. Compared with the traditional fusion algorithms such as IHS transform, Principal Component Analysis (PCA), Wavelet Transform (WT) and Brovey, the fusion images generated by the proposed algorithm have good visual effects; in addition, the proposed algorithm has better performance in terms of Peak-Signal-to-Noise Ratio (PSNR), spectral distortion and information entropy. Fusion images obtained by midway image equalization maintain spatial information well and have little spectral distortion.

The existing super-resolution reconstruction algorithms only consider the gray information of image patches, but ignores the texture information, and most nonlocal methods emphasize the nonlocal information without considering the local information. In view of these disadvantages, an image super-resolution reconstruction algorithm combined with compressed sensing and nonlocal information was proposed. Firstly, the similarity between pixels was calculated according to the structural features of image patches, and both the gray and the texture information was considered. Then, the weight of similar pixels was evaluated by merging the local and nonlocal information, and a regularization term combining the local and nonlocal information was constructed. Finally, the nonlocal information was introduced into the compressed sensing framework, and the sparse representation coefficients were solved by the iterative shrinkage algorithm. Experimental results demonstrate that the proposed algorithm outperforms other learning-based algorithms in terms of improved Peak Signal-to-Noise Ratio and Structural Similarity, and it can better recover the fine textures and effectively suppress the noise.