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A Review of Remote Sensing Image Dehazing
Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, nume...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201244/ https://www.ncbi.nlm.nih.gov/pubmed/34200320 http://dx.doi.org/10.3390/s21113926 |
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author | Liu, Juping Wang, Shiju Wang, Xin Ju, Mingye Zhang, Dengyin |
author_facet | Liu, Juping Wang, Shiju Wang, Xin Ju, Mingye Zhang, Dengyin |
author_sort | Liu, Juping |
collection | PubMed |
description | Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated. |
format | Online Article Text |
id | pubmed-8201244 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82012442021-06-15 A Review of Remote Sensing Image Dehazing Liu, Juping Wang, Shiju Wang, Xin Ju, Mingye Zhang, Dengyin Sensors (Basel) Review Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated. MDPI 2021-06-07 /pmc/articles/PMC8201244/ /pubmed/34200320 http://dx.doi.org/10.3390/s21113926 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 | Review Liu, Juping Wang, Shiju Wang, Xin Ju, Mingye Zhang, Dengyin A Review of Remote Sensing Image Dehazing |
title | A Review of Remote Sensing Image Dehazing |
title_full | A Review of Remote Sensing Image Dehazing |
title_fullStr | A Review of Remote Sensing Image Dehazing |
title_full_unstemmed | A Review of Remote Sensing Image Dehazing |
title_short | A Review of Remote Sensing Image Dehazing |
title_sort | review of remote sensing image dehazing |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201244/ https://www.ncbi.nlm.nih.gov/pubmed/34200320 http://dx.doi.org/10.3390/s21113926 |
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