Subpixel Mapping Using Markov Random Field With Multiple Spectral Constraints From Subpixel Shifted Remote Sensing Images

Subpixel mapping (SPM) is a promising technique to increase the spatial resolution of land cover maps. Markov random field (MRF)-based SPM has the advantages of considering spatial and spectral constraints simultaneously. In the conventional MRF, only the spectral information of one observed coarse spatial resolution image is utilized, which limits the SPM accuracy. In this letter, supplementary information from subpixel shifted remote sensing images (SSRSI) is used with MRF to produce more accurate SPM results. That is, spectral information from SSRSI is incorporated into the likelihood energy function of MRF to provide multiple spectral constraints. Simulated and real images were tested with the subpixel/pixel spatial attraction model, Hopfield neural networks (HNNs), HNN with SSRSI, image interpolation then hard classification, conventional MRF, and proposed MRF with SSRSI based SPM methods. Results showed that the proposed method can generate the most accurate SPM results among these methods.