Landscape structure based super-resolution mapping from remotely sensed imagery

Spatial resolution is one of the central issues in land cover mapping from remote sensing imagery, and sub-pixel land cover mapping is difficult and challenging in this domain. Soft classification can provide more information than hard classification. However, the spatial location of land cover compositions within each pixel is unknown. To solve this problem, super-resolution mapping methods have been developed in recent years. In this paper, a landscape structure based approach for super-resolution land cover mapping is introduced to generate super-resolution land cover maps from remote sensing data. The method was used to map simulated target images and a real landscape and the results indicate that this approach has the ability to reconstruct complicated landscape with linear features, and large or small patches relative to the pixel size.