# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
###### Abstract  
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. W 202010 7313 10.3390/app10207313 10.3390/app10207313 11 September 20202015 October 20202019 October  
multispectral images with low spatial resolution. Consequently, the need for multispectral images with higher spatial resolutions has increased, and efforts to produce high-spatial-resolution multispectral images that fuse panchromatic images and multispectral images have developed various algorithms to accomplish these tasks. Such techniques are termed pansharpening or image fusion in the remote sensing field [2; 3]. The pansharpening technique can be divided into component substitution (CS)- or multiresolution analysis (MRA)-based techniques according to the method used to extract the spatial details [4]. The CS-based technique produces synthetic intensity images similar to the spectral characteristics of panchromatic images through weighted combination with multispectral images. [PERSON] et al. [5] proposed an intensity-hue-saturation (IHS) pansharpening technique using an inverse transform method after first transforming the red, green, and blue (RGB) components of images into the IHS color system and replacing the intensity band with panchromatic image. However, applications for this technique were limited due to the subsequent development of sensors that could acquire multispectral images of more than four bands [6]. Other techniques were proposed that transformed images into other spectral systems through various spectral transformation methods, such as principal component analysis (PCA) and Gram-Schmidt (GS), and the images that showed the greatest similarity to panchromatic images were applied in pansharpening [7; 8]. In contrast to CS-based techniques, which produce optimal synthetic images using multispectral images, the MRA-based technique employs high-resolution panchromatic images whose resolution is reduced in the optimal panchromatic images. MRA-based techniques can be divided into wavelet-based image filtering and image pyramid utilization. The discrete wavelet transform (DWT) and a trous wavelet transform (ATWT) are typical wavelet-based pansharpening techniques that achieve fusion by extracting high-frequency information from panchromatic images [9; 10].