Pan-sharpening quality investigation of PLÉIADES-1A images

Abstract

Optical remote sensing satellites obtain MS and Pan images simultaneously over the same coverage area. Remote sensing and image processing communities are working on different pan-sharpening methods capable of taking advantage of MS and Pan images. Each remote sensing system has its own advantages and disadvantages, leading to the question "Which pan-sharpening method should be used for which type of imagery?"The aim of this research is to investigate the pan-sharpening performance of Pléiades-1Aimages. For this purpose, pan-sharpened images were generated using PCA, HIS and Brovey Transform which are the most popular pan-sharpening methods. Then, the pansharpened images were evaluated quantitatively using Correlation Coefficient, Root Mean Square Error, RASE (Relative Average Spectral Error), SAM (Spectral Angle Mapper) and ERGAS (Erreur Relative Globale Adimensionnelle de Synthése). In addition, pansharpened images were evaluated qualitatively by taking object availability and completeness into consideration.

Pan Sharpening Quality Investigation of Turkish in-Operation Remote Sensing Satellites: Applications With Rasat and Göktürk-2 Images

“…Computation of CC, RASE, SAM, RMSE and ERGAS metrics are performed using SharpQ, which is presented in (Ozendi et al, 2015). These metrics were calculated for each pan-sharpened image as the original MS image was taken as the reference dataset.…”

“…In this study, we aim to analyse pan-sharpening performance of RASAT and GÖKTÜRK-2. For this purpose, we used SharpQ tool developed by (Ozendi et al, 2015) which is capable of generating pan-sharpened images using PCA (Principal Component Analysis), IHS (Intensity Hue Saturation) and Brovey transform algorithms. This tool is also capable of calculating quality metrics CC, RASE, SAM, RMSE and ERGAS after generating the pan-sharpened images.…”