A Review of Remote Sensing Image Dehazing.

Liu, J; Wang, S; Wang, X; Ju, M; Zhang, D

A Review of Remote Sensing Image Dehazing.

Liu, J Wang, S Wang, X Ju, M

Zhang, D

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Sensors (Basel, Switzerland), 2021, 21, (11), pp. 3926-3926
Issue Date:: 2021-06-07

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Field	Value	Language
dc.contributor.author	Liu, J
dc.contributor.author	Wang, S
dc.contributor.author	Wang, X
dc.contributor.author	Ju, M https://orcid.org/0000-0003-4378-3781
dc.contributor.author	Zhang, D
dc.date.accessioned	2021-10-06T22:15:23Z
dc.date.available	2021-06-02
dc.date.available	2021-10-06T22:15:23Z
dc.date.issued	2021-06-07
dc.identifier.citation	Sensors (Basel, Switzerland), 2021, 21, (11), pp. 3926-3926
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/150871
dc.description.abstract	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.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI AG
dc.relation.ispartof	Sensors (Basel, Switzerland)
dc.relation.isbasedon	10.3390/s21113926
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.title	A Review of Remote Sensing Image Dehazing.
dc.type	Journal Article
utslib.citation.volume	21
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2021-10-06T22:15:16Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	11

Abstract:

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.

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http://hdl.handle.net/10453/150871