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An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images

Since remote sensing images are one of the main sources for people to obtain required information, the quality of the image becomes particularly important. Nevertheless, noise often inevitably exists in the image, and the targets are usually blurred by the acquisition of the imaging system, resultin...

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Detalles Bibliográficos
Autores principales: Zheng, Liangliang, Xu, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434460/
https://www.ncbi.nlm.nih.gov/pubmed/34502575
http://dx.doi.org/10.3390/s21175684
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author Zheng, Liangliang
Xu, Wei
author_facet Zheng, Liangliang
Xu, Wei
author_sort Zheng, Liangliang
collection PubMed
description Since remote sensing images are one of the main sources for people to obtain required information, the quality of the image becomes particularly important. Nevertheless, noise often inevitably exists in the image, and the targets are usually blurred by the acquisition of the imaging system, resulting in the degradation of quality of the images. In this paper, a novel preprocessing algorithm is proposed to simultaneously smooth noise and to enhance the edges, which can improve the visual quality of remote sensing images. It consists of an improved adaptive spatial filter, which is a weighted filter integrating functions of both noise removal and edge sharpness. Its processing parameters are flexible and adjustable relative to different images. The experimental results confirm that the proposed method outperforms the existing spatial algorithms both visually and quantitatively. It can play an important role in the remote sensing field in order to achieve more information of interested targets.
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spelling pubmed-84344602021-09-12 An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images Zheng, Liangliang Xu, Wei Sensors (Basel) Article Since remote sensing images are one of the main sources for people to obtain required information, the quality of the image becomes particularly important. Nevertheless, noise often inevitably exists in the image, and the targets are usually blurred by the acquisition of the imaging system, resulting in the degradation of quality of the images. In this paper, a novel preprocessing algorithm is proposed to simultaneously smooth noise and to enhance the edges, which can improve the visual quality of remote sensing images. It consists of an improved adaptive spatial filter, which is a weighted filter integrating functions of both noise removal and edge sharpness. Its processing parameters are flexible and adjustable relative to different images. The experimental results confirm that the proposed method outperforms the existing spatial algorithms both visually and quantitatively. It can play an important role in the remote sensing field in order to achieve more information of interested targets. MDPI 2021-08-24 /pmc/articles/PMC8434460/ /pubmed/34502575 http://dx.doi.org/10.3390/s21175684 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zheng, Liangliang
Xu, Wei
An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images
title An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images
title_full An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images
title_fullStr An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images
title_full_unstemmed An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images
title_short An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images
title_sort improved adaptive spatial preprocessing method for remote sensing images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434460/
https://www.ncbi.nlm.nih.gov/pubmed/34502575
http://dx.doi.org/10.3390/s21175684
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