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Super-resolution mapping (SRM) is a technique to obtain sub-pixel resolution thematic map (SRTM). Soft-then-hard SRM (STHSRM) is an important SRM algorithm due to its simple physical meaning. The soft classification errors may affect the SRTM derived by STHSRM. To overcome this problem, the maximum a posteriori probability (MAP) super-resolution then hard classification (MTC) algorithm has been proposed. However, the prior information of the original image is difficult to utilize in MTC. To solve this issue, a novel method based on pansharpening then hard classification (PTC) is proposed to improve SRTM. The pansharpening technique is applied to the original coarse image to obtain the improved resolution image by suppling more prior information. The SRTM is then derived from the improved resolution image by hard classification. Not only does PTC inherit the advantages of MTC that avoids soft classification errors, but it can also incorporate more prior information from the original image into the process. Experiments based on real remote sensing images show that the proposed method can produce higher mapping accuracy than the STHSRM and MTC. It is shown that the PTC has the percentage correctly classified (PCC) in the range from 89.62% to 95.92% for the experimental dataset.

Utilizing Pansharpening Technique to Produce Sub-Pixel Resolution Thematic Map from Coarse Remote Sensing Image