{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T23:42:46Z","timestamp":1773272566323,"version":"3.50.1"},"reference-count":132,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NaLamKI\u2014Nachhaltige Landwirtschaft mittels K\u00fcnstlicher Intelligenz","award":["01MK21003J"],"award-info":[{"award-number":["01MK21003J"]}]},{"name":"Federal Ministry for Economics and Climate Action","award":["01MK21003J"],"award-info":[{"award-number":["01MK21003J"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Image preprocessing and fusion are commonly used for enhancing remote-sensing images, but the resulting images often lack useful spatial features. As the majority of research on image fusion has concentrated on the satellite domain, the image-fusion task for Unmanned Aerial Vehicle (UAV) images has received minimal attention. This study investigated an image-improvement strategy by integrating image preprocessing and fusion tasks for UAV images. The goal is to improve spatial details and avoid color distortion in fused images. Techniques such as image denoising, sharpening, and Contrast Limited Adaptive Histogram Equalization (CLAHE) were used in the preprocessing step. The unsharp mask algorithm was used for image sharpening. Wiener and total variation denoising methods were used for image denoising. The image-fusion process was conducted in two steps: (1) fusing the spectral bands into one multispectral image and (2) pansharpening the panchromatic and multispectral images using the PanColorGAN model. The effectiveness of the proposed approach was evaluated using quantitative and qualitative assessment techniques, including no-reference image quality assessment (NR-IQA) metrics. In this experiment, the unsharp mask algorithm noticeably improved the spatial details of the pansharpened images. No preprocessing algorithm dramatically improved the color quality of the enhanced images. The proposed fusion approach improved the images without importing unnecessary blurring and color distortion issues.<\/jats:p>","DOI":"10.3390\/rs16050874","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T07:36:21Z","timestamp":1709278581000},"page":"874","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network"],"prefix":"10.3390","volume":"16","author":[{"given":"Sourav","family":"Modak","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence in Agricultural Engineering, University of Hohenheim, Garbenstra\u00dfe 9, Stuttgart, 70599 Baden-Wuerttemberg, Germany"}]},{"given":"Jonathan","family":"Heil","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence in Agricultural Engineering, University of Hohenheim, Garbenstra\u00dfe 9, Stuttgart, 70599 Baden-Wuerttemberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1808-9758","authenticated-orcid":false,"given":"Anthony","family":"Stein","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence in Agricultural Engineering, University of Hohenheim, Garbenstra\u00dfe 9, Stuttgart, 70599 Baden-Wuerttemberg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"463","DOI":"10.2478\/intag-2013-0017","article-title":"Effect of drought and heat stresses on plant growth and yield: A review","volume":"27","author":"Lipiec","year":"2013","journal-title":"Int. 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