Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm

The detection of changes in optical remote sensing images under the interference of thin clouds is studied for the first time in this paper. First, the optical remote sensing image is subjected to thin cloud removal processing, and then the processed remote sensing image is subjected to image change...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 19; H. 9; S. 1972
Hauptverfasser:	Yang, Xiaoqian, Jia, Zhenhong, Yang, Jie, Kasabov, Nikola
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 26.04.2019 MDPI
Schlagworte:	Algorithms Atmospheric aerosols change detection Clouds Clustering combination difference map FCM clustering Fog International conferences Noise optical remote sensing image Remote sensing Satellites thin cloud removal unsupervised Wavelet transforms China combination difference map change detection thin cloud removal optical remote sensing image unsupervised FCM clustering
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	The detection of changes in optical remote sensing images under the interference of thin clouds is studied for the first time in this paper. First, the optical remote sensing image is subjected to thin cloud removal processing, and then the processed remote sensing image is subjected to image change detection. Based on the analysis of the characteristics of thin cloud images, a method for removing thin clouds based on wavelet coefficient substitution is proposed in this paper. Based on the change in the wavelet coefficient, the high- and low-frequency parts of the remote sensing image are replaced separately, and the low-frequency clouds are suppressed while maintaining the high-frequency detail of the image, which achieves good results. Then, an unsupervised change detection algorithm based on a combined difference graph and fuzzy c-means clustering algorithm (FCM) clustering is applied. First, the image is transformed into a logarithmic domain, and the image is denoised using Frost filtering. Then, the mean ratio method and the difference method are used to obtain two graph difference maps, and the combined difference graph method is used to obtain the final difference image. The experimental results show that the algorithm can effectively solve the problem of image change detection under thin cloud interference.
AbstractList	The detection of changes in optical remote sensing images under the interference of thin clouds is studied for the first time in this paper. First, the optical remote sensing image is subjected to thin cloud removal processing, and then the processed remote sensing image is subjected to image change detection. Based on the analysis of the characteristics of thin cloud images, a method for removing thin clouds based on wavelet coefficient substitution is proposed in this paper. Based on the change in the wavelet coefficient, the high- and low-frequency parts of the remote sensing image are replaced separately, and the low-frequency clouds are suppressed while maintaining the high-frequency detail of the image, which achieves good results. Then, an unsupervised change detection algorithm based on a combined difference graph and fuzzy c-means clustering algorithm (FCM) clustering is applied. First, the image is transformed into a logarithmic domain, and the image is denoised using Frost filtering. Then, the mean ratio method and the difference method are used to obtain two graph difference maps, and the combined difference graph method is used to obtain the final difference image. The experimental results show that the algorithm can effectively solve the problem of image change detection under thin cloud interference. The detection of changes in optical remote sensing images under the interference of thin clouds is studied for the first time in this paper. First, the optical remote sensing image is subjected to thin cloud removal processing, and then the processed remote sensing image is subjected to image change detection. Based on the analysis of the characteristics of thin cloud images, a method for removing thin clouds based on wavelet coefficient substitution is proposed in this paper. Based on the change in the wavelet coefficient, the high- and low-frequency parts of the remote sensing image are replaced separately, and the low-frequency clouds are suppressed while maintaining the high-frequency detail of the image, which achieves good results. Then, an unsupervised change detection algorithm based on a combined difference graph and fuzzy c-means clustering algorithm (FCM) clustering is applied. First, the image is transformed into a logarithmic domain, and the image is denoised using Frost filtering. Then, the mean ratio method and the difference method are used to obtain two graph difference maps, and the combined difference graph method is used to obtain the final difference image. The experimental results show that the algorithm can effectively solve the problem of image change detection under thin cloud interference.The detection of changes in optical remote sensing images under the interference of thin clouds is studied for the first time in this paper. First, the optical remote sensing image is subjected to thin cloud removal processing, and then the processed remote sensing image is subjected to image change detection. Based on the analysis of the characteristics of thin cloud images, a method for removing thin clouds based on wavelet coefficient substitution is proposed in this paper. Based on the change in the wavelet coefficient, the high- and low-frequency parts of the remote sensing image are replaced separately, and the low-frequency clouds are suppressed while maintaining the high-frequency detail of the image, which achieves good results. Then, an unsupervised change detection algorithm based on a combined difference graph and fuzzy c-means clustering algorithm (FCM) clustering is applied. First, the image is transformed into a logarithmic domain, and the image is denoised using Frost filtering. Then, the mean ratio method and the difference method are used to obtain two graph difference maps, and the combined difference graph method is used to obtain the final difference image. The experimental results show that the algorithm can effectively solve the problem of image change detection under thin cloud interference.
Author	Jia, Zhenhong Yang, Jie Yang, Xiaoqian Kasabov, Nikola
AuthorAffiliation	3 Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, 1020 Auckland, New Zealand; nkasabov@aut.ac.nz 1 College of Information Science and Engineering, Xinjiang University, Urumuqi 830046, China; m15099106737@163.com 2 Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China; Jieyang@sjtu.edu.cn
AuthorAffiliation_xml	– name: 1 College of Information Science and Engineering, Xinjiang University, Urumuqi 830046, China; m15099106737@163.com – name: 3 Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, 1020 Auckland, New Zealand; nkasabov@aut.ac.nz – name: 2 Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China; Jieyang@sjtu.edu.cn
Author_xml	– sequence: 1 givenname: Xiaoqian surname: Yang fullname: Yang, Xiaoqian – sequence: 2 givenname: Zhenhong surname: Jia fullname: Jia, Zhenhong – sequence: 3 givenname: Jie surname: Yang fullname: Yang, Jie – sequence: 4 givenname: Nikola surname: Kasabov fullname: Kasabov, Nikola
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31035518$$D View this record in MEDLINE/PubMed
BookMark	eNplkklrGzEUgIeS0iztoX-gCHppD26k0SzSpRDczRAINAk9Ci1PtsyM5EqagP99ZTsJSXrS8j59ek9Pp9WRDx6q6j3BXyjl-DwRjjnhff2qOiFN3cxYXeOjJ_Pj6jSlNcY1pZS9qY4pwbRtCTuptvOV9EtA3yCDzi54FCy62mSn5YB-wxgyoGvwyfklWoxyR7qUp6jAILVFNyvn0XwIk0G3e-aPvIMBMpoHsNZpBz6j60ml7PK0118MyxBdXo1vq9dWDgne3Y9n1e2P7zfzX7PLq5-L-cXlTDcdz7O2NUYpzdpaW9tqVdbaGK5wD0rTlgAzvdWStX2HuQXTU2b72gDpFO8Ms_SsWhy8Jsi12EQ3yrgVQTqx3whxKWQs9Q4gbAMESN8wbnUjlZRKMWuhk31jdQkW19eDazOpEYwu1UU5PJM-j3i3EstwJ7qWctrzIvh0L4jh7wQpi9ElDcMgPYQpibou1_eEMlzQjy_QdZiiL08laopJt4OaQn14mtFjKg8dLsD5AdAxpBTBCu2y3LWiJOgGQbDY_SHx-IfKic8vTjxI_2f_ARAeyWM
CitedBy_id	crossref_primary_10_1109_TGRS_2024_3519810 crossref_primary_10_3390_s21134369 crossref_primary_10_3390_rs12152355 crossref_primary_10_3390_universe11090282 crossref_primary_10_1155_2021_2610887
Cites_doi	10.1109/JSTARS.2017.2655101 10.1109/LGRS.2012.2228626 10.1109/TNNLS.2015.2435783 10.1109/IFSC.2013.6675656 10.1016/j.isprsjprs.2013.03.006 10.1016/j.eswa.2015.04.002 10.1109/TGRS.2009.2029095 10.1109/SOPO.2010.5504180 10.1109/PROC.1981.11935 10.1109/LGRS.2009.2025059 10.1016/j.patcog.2009.08.020 10.1109/IndiaCom.2014.6828169 10.1109/GeoInformatics.2011.5980963 10.1016/0098-3004(84)90020-7 10.1109/M2RSM.2011.5697411 10.1016/j.sigpro.2009.01.012 10.1016/j.sigpro.2017.07.023 10.1109/ICALIP.2016.7846593 10.1109/78.157290 10.1109/LGRS.2011.2167211 10.1109/36.981363 10.1109/ICAPR.2009.82 10.1109/LGRS.2009.2026188 10.1109/36.911113 10.1109/36.210462 10.1109/ICNETS2.2017.8067921 10.1016/j.asoc.2012.03.060 10.1023/A:1021986309149
ContentType	Journal Article
Copyright	2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2019 by the authors. 2019
Copyright_xml	– notice: 2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2019 by the authors. 2019
DBID	AAYXX CITATION NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI 7X8 5PM DOA
DOI	10.3390/s19091972
DatabaseName	CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection (ProQuest) ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef PubMed Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	CrossRef MEDLINE - Academic PubMed Publicly Available Content Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1424-8220
ExternalDocumentID	oai_doaj_org_article_f4e1e17489fc4abaabb8ffe6a74fcf4e PMC6539379 31035518 10_3390_s19091972
Genre	Journal Article
GeographicLocations	China
GeographicLocations_xml	– name: China
GrantInformation_xml	– fundername: the International Cooperative Research and Personnel Training Projects of the Ministry of Education of the People's Republic of China grantid: 2014-2029 and 2016-2196 – fundername: National Natural Science Foundation of China grantid: 61665012
GroupedDBID	--- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M ALIPV NPM 3V. 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI 7X8 PUEGO 5PM
ID	FETCH-LOGICAL-c469t-55ddbbc852cff5cb55dcdd9b07ebc351e8d7fca857609fed738f72de16b96d8f3
IEDL.DBID	DOA
ISICitedReferencesCount	6
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000469766800016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1424-8220
IngestDate	Fri Oct 03 12:53:15 EDT 2025 Tue Nov 04 01:37:06 EST 2025 Thu Sep 04 18:56:10 EDT 2025 Tue Oct 07 07:10:07 EDT 2025 Thu Apr 03 07:08:19 EDT 2025 Sat Nov 29 07:18:02 EST 2025 Tue Nov 18 19:58:33 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	9
Keywords	combination difference map change detection thin cloud removal optical remote sensing image unsupervised FCM clustering
Language	English
License	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 (http://creativecommons.org/licenses/by/4.0/).
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c469t-55ddbbc852cff5cb55dcdd9b07ebc351e8d7fca857609fed738f72de16b96d8f3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
OpenAccessLink	https://doaj.org/article/f4e1e17489fc4abaabb8ffe6a74fcf4e
PMID	31035518
PQID	2301613804
PQPubID	2032333
ParticipantIDs	doaj_primary_oai_doaj_org_article_f4e1e17489fc4abaabb8ffe6a74fcf4e pubmedcentral_primary_oai_pubmedcentral_nih_gov_6539379 proquest_miscellaneous_2217471380 proquest_journals_2301613804 pubmed_primary_31035518 crossref_citationtrail_10_3390_s19091972 crossref_primary_10_3390_s19091972
PublicationCentury	2000
PublicationDate	20190426
PublicationDateYYYYMMDD	2019-04-26
PublicationDate_xml	– month: 4 year: 2019 text: 20190426 day: 26
PublicationDecade	2010
PublicationPlace	Switzerland
PublicationPlace_xml	– name: Switzerland – name: Basel
PublicationTitle	Sensors (Basel, Switzerland)
PublicationTitleAlternate	Sensors (Basel)
PublicationYear	2019
Publisher	MDPI AG MDPI
Publisher_xml	– name: MDPI AG – name: MDPI
References	Chen (ref_9) 2010; 43 Hussain (ref_12) 2013; 80 Gong (ref_23) 2012; 9 Tang (ref_5) 2013; 10 ref_31 Liu (ref_17) 2018; 45 Shensa (ref_21) 1992; 40 Casillas (ref_28) 2003; 18 Tee (ref_30) 2002; 40 Villasensor (ref_24) 1993; 31 ref_19 ref_18 ref_15 Hesen (ref_33) 2014; 35 Frost (ref_22) 2005; 69 Zhu (ref_20) 2007; 6625 Zhang (ref_7) 2009; 89 Gong (ref_3) 2015; 27 Celik (ref_10) 2009; 6 ref_1 Li (ref_14) 2017; 10 Zhou (ref_16) 2015; 3 Celik (ref_11) 2009; 6 ref_29 ref_27 Celik (ref_2) 2010; 48 ref_8 Hazel (ref_13) 2001; 39 Bezdek (ref_26) 1984; 10 ref_4 Bhowmik (ref_32) 2015; 42 ref_6 Mishra (ref_25) 2012; 12
References_xml	– volume: 10 start-page: 1870 year: 2017 ident: ref_14 article-title: Removal of optically thick clouds from High-Resolution satellite imagery using dictionary group learning and interdictionary nonlocal joint sparse coding publication-title: IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. doi: 10.1109/JSTARS.2017.2655101 – volume: 10 start-page: 1060 year: 2013 ident: ref_5 article-title: Fault-tolerant building change detection from urban high-resolution remote sensing imagery publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2012.2228626 – volume: 27 start-page: 125 year: 2015 ident: ref_3 article-title: Change detection in synthetic aperture radar images based on deep neural networks publication-title: IEEE Trans. Neural Networks Learn. Syst. doi: 10.1109/TNNLS.2015.2435783 – ident: ref_27 doi: 10.1109/IFSC.2013.6675656 – volume: 80 start-page: 91 year: 2013 ident: ref_12 article-title: Change detection from remotely sensed images: From pixel-based to object-based approaches publication-title: ISPRS J. Photogram. Remote Sens. doi: 10.1016/j.isprsjprs.2013.03.006 – volume: 42 start-page: 6075 year: 2015 ident: ref_32 article-title: An effective power quality classifier using wavelet transform and support vector machines publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2015.04.002 – volume: 48 start-page: 1199 year: 2010 ident: ref_2 article-title: Unsupervised change detection for satellite images using dual-tree complex wavelet transform publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2009.2029095 – volume: 45 start-page: Z6 year: 2018 ident: ref_17 article-title: Study and Application of Improved Retinex Algorithm in Image Defogging publication-title: Computer Science – ident: ref_29 doi: 10.1109/SOPO.2010.5504180 – volume: 69 start-page: 133 year: 2005 ident: ref_22 article-title: An adaptive filter for smoothing noisy radar images publication-title: Proc. IEEE doi: 10.1109/PROC.1981.11935 – volume: 6 start-page: 772 year: 2009 ident: ref_11 article-title: Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and -Means Clustering publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2009.2025059 – volume: 43 start-page: 579 year: 2010 ident: ref_9 article-title: Invariant pattern recognition using contourlets and AdaBoost publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2009.08.020 – ident: ref_1 doi: 10.1109/IndiaCom.2014.6828169 – ident: ref_15 doi: 10.1109/GeoInformatics.2011.5980963 – volume: 10 start-page: 191 year: 1984 ident: ref_26 article-title: FCM: The fuzzy c-means clustering algorithm publication-title: Comput. Geosci. doi: 10.1016/0098-3004(84)90020-7 – ident: ref_6 doi: 10.1109/M2RSM.2011.5697411 – volume: 89 start-page: 1334 year: 2009 ident: ref_7 article-title: Multifocus image fusion using the nonsubsampled contourlet transform publication-title: Signal Process. doi: 10.1016/j.sigpro.2009.01.012 – ident: ref_8 doi: 10.1016/j.sigpro.2017.07.023 – ident: ref_18 doi: 10.1109/ICALIP.2016.7846593 – volume: 40 start-page: 2464 year: 1992 ident: ref_21 article-title: The discrete wavelet transform: Wedding the a trous and Mallat algorithms publication-title: IEEE Trans. Signal Process. doi: 10.1109/78.157290 – volume: 35 start-page: 42 year: 2014 ident: ref_33 article-title: Based on NSCT Combination with FCM Multitemporal Remote Sensing Image Change Detection publication-title: Laser J. – volume: 9 start-page: 307 year: 2012 ident: ref_23 article-title: A neighborhood-based ratio approach for change detection in SAR images publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2011.2167211 – volume: 40 start-page: 210 year: 2002 ident: ref_30 article-title: Haze detection and removal in high resolution satellite image with wavelet analysis publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.981363 – ident: ref_4 doi: 10.1109/ICAPR.2009.82 – ident: ref_19 – volume: 6 start-page: 820 year: 2009 ident: ref_10 article-title: Multiscale Change detection in multitemporal satellite images publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2009.2026188 – volume: 39 start-page: 553 year: 2001 ident: ref_13 article-title: Object-level change detection in spectral imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.911113 – volume: 31 start-page: 227 year: 1993 ident: ref_24 article-title: Change detection on Alaska’s North Slope using repeat-pass ERS-1 SAR images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.210462 – ident: ref_31 doi: 10.1109/ICNETS2.2017.8067921 – volume: 3 start-page: 14 year: 2015 ident: ref_16 article-title: An algorithm of cloud removal for remote sensing image based on improved homomorphic Filtering publication-title: Radio Eng. – volume: 6625 start-page: 241 year: 2007 ident: ref_20 article-title: An improved approach to remove cloud and mist from remote sensing digital images based on mallat algorithm publication-title: J. Remote Sens. – volume: 12 start-page: 2683 year: 2012 ident: ref_25 article-title: Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2012.03.060 – volume: 18 start-page: 155 year: 2003 ident: ref_28 article-title: Fuzzy control of HVAC systems optimized by genetic algorithms publication-title: Appl. Intell. doi: 10.1023/A:1021986309149
SSID	ssj0023338
Score	2.2908046
Snippet	The detection of changes in optical remote sensing images under the interference of thin clouds is studied for the first time in this paper. First, the optical...
SourceID	doaj pubmedcentral proquest pubmed crossref
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source
StartPage	1972
SubjectTerms	Algorithms Atmospheric aerosols change detection Clouds Clustering combination difference map FCM clustering Fog International conferences Noise optical remote sensing image Remote sensing Satellites thin cloud removal unsupervised Wavelet transforms
SummonAdditionalLinks	– databaseName: ProQuest Publicly Available Content Database dbid: PIMPY link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9NAEF5BygEOvAuGghbEgYsVv3d9QiVQUQlKxLOcrH3MppVSOyQOUv89M2vHNKjixNHekTXWzM5jd-Ybxl4IdLJxAja0SpZhVkASlhgWhZlz0tlIgvLtY9_ei6MjeXxcTvv26FVfVrmxid5Qd2jPVLeNRnhsG0Mn5mMMnDFUSWWUvVr8DGmGFN219gM1rrIdAt6KRmxnevhh-mNIwFLMxzp0oRRT_fEKnWFJY7e2fJKH7r8s3vy7bPKCHzq49X__4Da72cejfL9ToDvsCtR32Y0LKIX32HnXgcDfQOvrtmreOP5x4Q_B-SdAWQP_THXw9YwfnimiRN1ZLzVYrs85TQblk3mzttzXJ_DvioZdtHzSgMevQH452S9ftECf35_PkNP25Ow--3rw9svkXdhPbAgNptltmOfWam1knhjncqPx2Vhb6kiANmkeg7TCGSUxyYlKB1ak0onEQlzosrDSpbtsVDc1PGS8iIyWinoE8jTTrlCgSspPbSqkSrIiYC83MqtMD2dOUzXmFaY1JN5qEG_Ang-kiw7D4zKi1yT4gYBgt_2LZjmr-l1cuQxiiAmwx5lMaaW0ls5BoUTmDC4GbG8j-qq3Bavqj6QD9mxYxl1MVzOqhmaNNJQZCiIK2INOywZOaBIc4eYFTGzp3xar2yv16YlHCifc4VSUj_7N1mN2HcNAf0eWFHts1C7X8IRdM7_a09Xyab-JfgM7yjPE priority: 102 providerName: ProQuest
Title	Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm
URI	https://www.ncbi.nlm.nih.gov/pubmed/31035518 https://www.proquest.com/docview/2301613804 https://www.proquest.com/docview/2217471380 https://pubmed.ncbi.nlm.nih.gov/PMC6539379 https://doaj.org/article/f4e1e17489fc4abaabb8ffe6a74fcf4e
Volume	19
WOSCitedRecordID	wos000469766800016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources (ISSN International Center) customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LbxMxELagcIAD4s1CiQziwGXVfdt7bEMqKtEQlVc4rfwYt5HS3SrZIPXCb2fGu4kSVIkLF0trj1Zez9iebz3-hrF3AjfZOAEbWiXLMCsgCUt0i8LMOelsJEH562PfP4nxWE6n5WQr1RfFhHX0wN3AHbgMYoiJI8WZTGmltJbOQaFE5gw20uobiXINpnqolSLy6niEUgT1B0vc9kpKsLWz-3iS_ps8y78DJLd2nOOH7EHvKvLDrouP2C2oH7P7WwSCT9h1dzmAf4DWh1TVvHH885X_P83PANUA_AuFqNfn_ORSkSSqdbXQYLm-5pS0kw_nzcpyHzrAfyjKQ9HyYQOeWgI7xmlp8fEE9PrD-XmzmLUXl0_Zt-PR1-HHsE-mEBpEwG2Y59ZqbWSeGOdyo_HZWFvqSIA2aR6DtMIZJRF_RKUDK1LpRGIhLnRZWOnSZ2yvbmp4wXgRGS0Vhe_naaZdoUCVBB1tKqRKsiJg79eDXJmeaZwSXswrRBykj2qjj4C93YhedfQaNwkdkaY2AsSI7SvQTqreTqp_2UnA9td6rvppuqwQf6HHm8ooC9ibTTNOMDo1UTU0K5Qh0CZIKGDPO7PY9ISStBGlXcDEjsHsdHW3pZ5deBJvogRORfnyf3zbK3YP_Th_yJUU-2yvXazgNbtrfrWz5WLAboup8KUcsDtHo_HkbOBnC5anv0dYNzk5nfz8A0L6Igg
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFAk48H4YCiwIJC5WHb92fUCopFSNmoYKCpST2WdaKbVD4oDyp_iN7Kwd06CKWw8c7R1Z6_W3szPe2e8DeEHtItsNtfIVZ5kfpzr0MxsW-bExzKiAae6Oj30e0OGQHR1lB2vwa3kWBssqlz7ROWpVSvxHvmlDZRucRCyI30y--6gahburSwmNGhZ7evHTpmyz1_1t-31fhuHOu8Pert-oCvjSpoKVnyRKCSFZEkpjEinstVQqEwHVQkZJVzNFjeTMBuJBZrSiETM0VLqbiixVzET2uZdgPbZgDzqwftDfP_japniRzfhq_qIoyoLNmV1uMxT2Wln1nDjAeRHt34WZZ1a6nRv_2xjdhOtNTE226klwC9Z0cRuunWFavAOL-hQF2daVqz0rSGnI-4n7kU8-aItXTT5iLX8xIv1TjpYW__Op0IqIBUF1U9Ibl3NFXI0F-cJRsKMivVI7Dg47kgR9sCu8wMdvjUd2ZKrj07vw6ULe_R50irLQD4CkgRSM4zmHJIqFSbnmGebYKqKMh3HqwaslKnLZULKjMsg4t6kZAihvAeTB89Z0UvOQnGf0FqHVGiB1uLtRTkd544lyE-uu7iLpkJExF5wLwYzRKaexkbbRg40luPLGn83yP8jy4FnbbD0Rbi_xQpdza4PZLUUjD-7XOG57gmp2yP3nAV1B-EpXV1uKk2PHdo7cyRHNHv67W0_hyu7h_iAf9Id7j-CqDWvdnl-YbkCnms71Y7gsf1Qns-mTZsoS-HbRM-A3t5aJ6g
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VFCE48H4YCiwIJC5W_N71AaE2aUTUKkTl1ZvZZ1optUPigPLX-HXM2I5pUMWtB472jqz1-tvZGe_s9xHyisEi6wdGu1rw1I0SE7gphEVuZC232uNGVMfHvhyy0YgfH6fjLfJrfRYGyyrXPrFy1LpQ-I-8C6EyBCch96Kubcoixv3Bu9l3FxWkcKd1LadRQ-TArH5C-rZ4O-zDt34dBIP9T733bqMw4CpIC0s3jrWWUvE4UNbGSsK10jqVHjNShbFvuGZWCQ5BuZdao1nILQu08ROZJprbEJ57hWyzEJKeDtne2x-Nj9p0L4Tsr-YyCsPU6y5g6U1R5GtjBayEAi6Kbv8u0jy36g1u_c_jdZvcbGJtultPjjtky-R3yY1zDIz3yKo-XUH7pqxq0nJaWPphVv3gp0cGcGzoR6zxzyd0eCbQEubFci6NpnJFUfWU9qbFUtOq9oJ-FSjkUdJeYSpuDhhVir65KsjAx-9OJzAy5cnZffL5Ut79AenkRW4eEZp4SnKB5x_iMJI2EUakmHvrkHERRIlD3qwRkqmGqh0VQ6YZpGwIpqwFk0Netqazmp_kIqM9hFlrgJTi1Y1iPskaD5XZyPjGRzIiqyIhhZCSW2sSwSKroNEhO2ugZY2fW2R_UOaQF20zeCjcdhK5KZZgg1kvQyOHPKwx3fYEVe6QE9AhbAPtG13dbMlPTyoWdORUDln6-N_dek6uAeyzw-Ho4Am5DtFutRUYJDukU86X5im5qn6Up4v5s2b2UvLtsifAbw6YkoQ
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Change+Detection+of+Optical+Remote+Sensing+Image+Disturbed+by+Thin+Cloud+Using+Wavelet+Coefficient+Substitution+Algorithm&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Yang%2C+Xiaoqian&rft.au=Jia%2C+Zhenhong&rft.au=Yang%2C+Jie&rft.au=Kasabov%2C+Nikola&rft.date=2019-04-26&rft.eissn=1424-8220&rft.volume=19&rft.issue=9&rft_id=info:doi/10.3390%2Fs19091972&rft_id=info%3Apmid%2F31035518&rft.externalDocID=31035518
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon