A framework of mixed sparse representations for remote sensing images

In this paper, a new framework of mixed sparse representations (MSRs) is proposed for solving ill-conditioned problems with remote sensing images. In general, it is very difficult to find a common sparse representation for remote sensing images because of complicated ground features. Here we regard a remote sensing image as a combination of subimage of smooth, edges, and point-like components, respectively. Since each domain transformation method is capable of representing only a particular kind of ground object or texture, a group of domain transformations are used to sparsely represent each subimage. To demonstrate the effect of the framework of MSR for remote sensing images, MSR is regarded as a prior for maximum a posteriori when solving ill-conditioned problems such as classification and super resolution (SR), respectively. The experimental results show that not only the new framework of MSR can improve classification accuracy but also it can construct a much better high-resolution image than other common SR methods. The proposed framework MSR is a competitive candidate for solving other remote sensing images-related ill-conditioned problems.