Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images

Compared to using images in the visible and near-infrared (VNIR) wavelength range only, remotely sensed satellite imagery from the spectral wavelengths of both VNIR and shortwave infrared (SWIR), such as Sentinel-2A and Worldview-3, is more effective for analyzing various types of information for tasks such as land cover mapping, environmental monitoring and land use change detection. In this manuscript, a new sharpening technique to enhance the spatial resolution of Worldview-3 satellite imagery with various spatial and spectral resolutions is proposed. Selected and synthesized band schemes were used to produce optimal panchromatic images; then, sharpened images were generated by applying the Gram-Schmidt adaptive (GSA) and Gram-Schmidt 2 (GS2) techniques, which are component substitution (CS)- and multiresolution analysis (MRA)-based algorithms, respectively. In addition, to minimize the spectral distortion of the initial sharpened image, a postprocessing methodology for spectral distortion reduction was developed. Qualitative and quantitative evaluation of the sharpened images showed that the pansharpening performance using the GS2 technique based on the selected band scheme and spectral distortion reduction was the best. To confirm the usability of the SWIR band, supervised classification based on machine learning was performed on the pansharpened images obtained by applying the technique proposed in this study and on the pansharpened images obtained by the VNIR bands only. The classification accuracy of the results using SWIR bands was higher than that of VNIR bands only. In particular, it was confirmed that the accuracy of the classification of artificial facilities known to be effective for SWIR bands was greatly improved.