Soft-Then-Hard Super-Resolution Mapping Based on Pansharpening Technique for Remote Sensing Image

Abstract

Super-resolution mapping (SRM) technique can explore the spatial distribution information of land cover classes in mixed pixels for multispectral image (MSI) or hyspectral image (HSI). Soft-then-hard super-resolution mapping (STHSRM) is an important type of SRM technique. STHSRM first utilizes the subpixel sharpening to produce the high-resolution fractional images with the soft attribute values for each subpixel and then allocates the hard class labels to each subpixel. However, due to the low resolution in the original image, the fractional images are difficult to pick up the full spatial-spectral information from the original image. In this paper, pansharpening technique is utilized in STHSRM (STHSRM-PAN) to produce the fractional images with more spatial-spectral information, which improves the mapping results. First, the original low-resolution MSI or HSI and a panchromatic image (PAN) are fused by pansharpening technique to produce the improved resolution image with the high spectral resolution of MSI or HSI and the high spatial resolution of PAN. The high-resolution fractional images with more spatial-spectral information are then obtained by unmixing the improved resolution image. Finally, the class labels are assigned to each subpixel according to the soft attribute values from the high-resolution fractional images. Comparing with the state-of-the-art STHSRM algorithms, the STHSRM-PAN shows the best performance with the percentage correctly classified and Kappa coefficient (Kappa) in the three experimental results.