ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-X-4-W1-2022-691-2023 DECISION-BASED FUSION OF PANSHARPENED VHR SATELLITE IMAGES USING TWO-LEVEL ROLLING SELF-GUIDANCE FILTERING AND EDGE INFORMATION Samadzadegan F. 1 Toosi A. 1 Dadrass Javan F. 1 2 Stein A. 2 School of Surveying and Geospatial Engineering, University College of Engineering, University of Tehran, Tehran, Iran Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands 14 01 2023 X-4/W1-2022 691 698 Copyright: © 2023 F. Samadzadegan et al. 2023 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/X-4-W1-2022/691/2023/isprs-annals-X-4-W1-2022-691-2023.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/X-4-W1-2022/691/2023/isprs-annals-X-4-W1-2022-691-2023.pdf

Pan-sharpening (PS) fuses low-resolution multispectral (LR MS) images with high-resolution panchromatic (HR PAN) bands to produce HR MS data. Current PS methods either better maintain the spectral information of MS images, or better transfer the PAN spatial details to the MS bands. In this study, we propose a decision-based fusion method that integrates two basic pan-sharpened very-high-resolution (VHR) satellite imageries taking advantage of both images simultaneously. It uses two-level rolling self-guidance filtering (RSGF) and Canny edge detection. The method is tested on Worldview (WV)-2 and WV-4 VHR satellite images on the San Fransisco and New York areas, using four PS algorithms. Results indicate that the proposed method increased the overall spectral-spatial quality of the base pan-sharpened images by 7.2% and 9.8% for the San Fransisco and New York areas, respectively. Our method therefore effectively addresses decision-level fusion of different base pan-sharpened images.