Radiometric Normalization Using a Pseudo−Invariant Polygon Features−Based Algorithm with Contemporaneous Sentinel−2A and Landsat−8 OLI Imagery
As sensor parameters and atmospheric conditions create uncertainties for at−sensor radiation detection, radiometric consistency among satellite images is difficult to achieve. Relative radiometric normalization is a method that can improve multi−image consistency with accurate pseudo−invariant featu...
Saved in:
| Published in | Applied sciences Vol. 13; no. 4; p. 2525 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.02.2023
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 2076-3417 2076-3417 |
| DOI | 10.3390/app13042525 |
Cover
| Abstract | As sensor parameters and atmospheric conditions create uncertainties for at−sensor radiation detection, radiometric consistency among satellite images is difficult to achieve. Relative radiometric normalization is a method that can improve multi−image consistency with accurate pseudo−invariant features (PIFs), especially over large areas or long time series satellite images. Although there are algorithms that manually or automatically select PIFs, the spatial mismatch of satellite images can affect PIF extraction, particularly with artificial pixels. To alleviate this problem, we proposed to use Landsat−8 OLI as the reference image and Sentinel−2A as the subject image, to apply pseudo−invariant features−based algorithms with polygon features through the single−band and multiple−band regression. Compared to pseudo−invariant point features, hyperspectral library, and histogram matching approaches, the results demonstrate the superiority of the proposed algorithms with correlation coefficients of 0.9948 and 0.9945, and an RMSE of 0.0097 and 0.0095 with multiple− and single−band regression, respectively. We also found more accurate linear fitting and better shape matching through band scattering and reflectance frequency analysis. The proposed algorithms are a significant improvement in radiometric normalization, within artificial pixels, achieving spectral signature consistency. |
|---|---|
| AbstractList | As sensor parameters and atmospheric conditions create uncertainties for at−sensor radiation detection, radiometric consistency among satellite images is difficult to achieve. Relative radiometric normalization is a method that can improve multi−image consistency with accurate pseudo−invariant features (PIFs), especially over large areas or long time series satellite images. Although there are algorithms that manually or automatically select PIFs, the spatial mismatch of satellite images can affect PIF extraction, particularly with artificial pixels. To alleviate this problem, we proposed to use Landsat−8 OLI as the reference image and Sentinel−2A as the subject image, to apply pseudo−invariant features−based algorithms with polygon features through the single−band and multiple−band regression. Compared to pseudo−invariant point features, hyperspectral library, and histogram matching approaches, the results demonstrate the superiority of the proposed algorithms with correlation coefficients of 0.9948 and 0.9945, and an RMSE of 0.0097 and 0.0095 with multiple− and single−band regression, respectively. We also found more accurate linear fitting and better shape matching through band scattering and reflectance frequency analysis. The proposed algorithms are a significant improvement in radiometric normalization, within artificial pixels, achieving spectral signature consistency. |
| Audience | Academic |
| Author | Ma, Ying Lin, Yanzhen Yu, Yanmiao Lian, Yi Chen, Lei Zhang, Hu |
| Author_xml | – sequence: 1 givenname: Lei surname: Chen fullname: Chen, Lei – sequence: 2 givenname: Ying surname: Ma fullname: Ma, Ying – sequence: 3 givenname: Yi surname: Lian fullname: Lian, Yi – sequence: 4 givenname: Hu surname: Zhang fullname: Zhang, Hu – sequence: 5 givenname: Yanmiao surname: Yu fullname: Yu, Yanmiao – sequence: 6 givenname: Yanzhen surname: Lin fullname: Lin, Yanzhen |
| BookMark | eNp9Uc1u1DAYjFCRKKUnXsASR9jinyR2jsuKQqQVraCcoy_-CV4ldrAdquUJOHPgAfskdbsIVUhgS5-t8cxorHlaHDnvdFE8J_iMsQa_hnkmDJe0otWj4phiXq9YSfjRg_uT4jTGHc6rIUwQfFz8-gjK-kmnYCX64MMEo_0OyXqHPkfrBgToMupF-ZsfP1v3DYIFl9ClH_dDppxrSEvQMT--gagVWo-DDzZ9mdB1nmjjXdLT7AM47ZeIPmmXrNNj5tM1AqfQNo8IKQMCXWxb1E4w6LB_Vjw2MEZ9-vs8Ka7O315t3q-2F-_azXq7kiVmaVU2XLIeuOmrWouaqZISKhRgCsIYKivoFTDaM05JwypDq16AMo3RteKkYidFe7BVHnbdHOwEYd95sN094MPQQUhWjrrTfc9JXQuDRVkarkGzShnJatOAzFD2enXwWtwM-2sYxz-GBHd3DXUPGsr0Fwf6HPzXRcfU7fwSXP5sRzlvKsqxuGOdHVgD5AzWGZ8CyLyVnqzM_Rub8TWvSCMEEU0WkINABh9j0KaTNt33mYV2_EeUl39p_hf8FgRvxo4 |
| CitedBy_id | crossref_primary_10_1016_j_eswa_2023_122172 crossref_primary_10_3390_rs15184399 crossref_primary_10_3390_technologies11020046 crossref_primary_10_3390_s24072272 |
| Cites_doi | 10.3390/rs13193990 10.1016/j.rse.2006.03.008 10.1080/01431161.2022.2102951 10.1080/10106049.2017.1367424 10.1007/s11356-019-07216-1 10.1007/978-3-662-03978-6 10.1080/01431161.2016.1213922 10.1109/LGRS.2019.2899969 10.1016/j.atmosres.2020.105308 10.3390/rs5062763 10.3390/rs9121319 10.1016/j.isprsjprs.2018.11.007 10.1016/j.rse.2007.07.013 10.1080/15481603.2020.1799546 10.1016/j.isprsjprs.2022.10.019 10.1109/JSTARS.2020.3028062 10.3390/rs8050411 10.2747/1548-1603.49.5.755 10.1109/JSTARS.2021.3069919 10.1016/j.apm.2013.01.006 10.1016/0034-4257(91)90062-B 10.3390/s18124505 10.1007/s40314-015-0254-z 10.1080/01431160601086019 10.3390/rs14081777 10.1016/j.inffus.2004.12.002 10.3390/rs13163125 10.1016/j.compag.2019.104893 10.1016/j.ufug.2020.126675 10.1109/TGRS.2021.3063151 10.3390/rs61211810 10.1007/s12665-020-09220-y 10.2747/1548-1603.46.3.249 10.1016/j.isprsjprs.2015.05.002 10.1109/LGRS.2020.3047344 10.1080/17538947.2015.1111951 10.1080/01431161.2021.1934912 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2023 MDPI AG 2023 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: COPYRIGHT 2023 MDPI AG – notice: 2023 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION ABUWG AFKRA AZQEC BENPR CCPQU DWQXO PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS ADTOC UNPAY DOA |
| DOI | 10.3390/app13042525 |
| DatabaseName | CrossRef ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central (via ProQuest) ProQuest One Community College ProQuest Central Korea ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Unpaywall for CDI: Periodical Content Unpaywall Directory of Open Access Journals (DOAJ) |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: Openly Available Collection - DOAJ url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 3 dbid: BENPR name: ProQuest Central url: http://www.proquest.com/pqcentral?accountid=15518 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Sciences (General) |
| EISSN | 2076-3417 |
| ExternalDocumentID | oai_doaj_org_article_ebb71668f0844f7eae35dfc36f9ac844 10.3390/app13042525 A751988189 10_3390_app13042525 |
| GroupedDBID | .4S 2XV 5VS 7XC 8CJ 8FE 8FG 8FH AADQD AAFWJ AAYXX ADBBV ADMLS AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS APEBS ARCSS BCNDV BENPR CCPQU CITATION CZ9 D1I D1J D1K GROUPED_DOAJ IAO IGS ITC K6- K6V KC. KQ8 L6V LK5 LK8 M7R MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PROAC TUS ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI PRINS ADTOC IPNFZ RIG UNPAY |
| ID | FETCH-LOGICAL-c403t-497c3ba7fb56e863d42128da02a8ff2c5abda32b3721935f25b8adf9fe6d7153 |
| IEDL.DBID | DOA |
| ISSN | 2076-3417 |
| IngestDate | Fri Oct 03 12:50:57 EDT 2025 Sun Oct 26 03:26:35 EDT 2025 Mon Jun 30 07:32:49 EDT 2025 Mon Oct 20 16:22:00 EDT 2025 Thu Oct 16 04:42:39 EDT 2025 Thu Apr 24 22:54:52 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| License | cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c403t-497c3ba7fb56e863d42128da02a8ff2c5abda32b3721935f25b8adf9fe6d7153 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://doaj.org/article/ebb71668f0844f7eae35dfc36f9ac844 |
| PQID | 2779527085 |
| PQPubID | 2032433 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_ebb71668f0844f7eae35dfc36f9ac844 unpaywall_primary_10_3390_app13042525 proquest_journals_2779527085 gale_infotracacademiconefile_A751988189 crossref_citationtrail_10_3390_app13042525 crossref_primary_10_3390_app13042525 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-02-01 |
| PublicationDateYYYYMMDD | 2023-02-01 |
| PublicationDate_xml | – month: 02 year: 2023 text: 2023-02-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Applied sciences |
| PublicationYear | 2023 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Liu (ref_26) 2020; 13 Bonnet (ref_10) 2022; 19 Leach (ref_14) 2019; 164 Zhou (ref_24) 2016; 37 Elvidge (ref_29) 1995; 61 Silva (ref_37) 2013; 5 ref_13 Razzak (ref_16) 2023; 195 ref_35 ref_34 Rahman (ref_36) 2014; 6 Moghimi (ref_33) 2022; 60 Mansaray (ref_2) 2020; 57 Nazeer (ref_12) 2021; 249 Saradjian (ref_30) 2005; 6 Loiseau (ref_4) 2019; 82 ref_17 Sadeghi (ref_19) 2013; 37 Pu (ref_5) 2020; 53 Xu (ref_27) 2021; 42 Hall (ref_20) 1991; 35 Schroeder (ref_3) 2006; 103 Rahman (ref_7) 2015; 106 Syariz (ref_22) 2019; 147 Sadeghi (ref_21) 2017; 36 Lin (ref_25) 2019; 16 ref_23 Kim (ref_38) 2012; 49 Henchiri (ref_1) 2020; 27 Hong (ref_32) 2008; 29 Moghimi (ref_18) 2021; 14 Santra (ref_15) 2019; 34 Moghimi (ref_39) 2022; 43 Deliry (ref_6) 2020; 79 ref_9 ref_8 Canty (ref_28) 2008; 112 Yan (ref_11) 2016; 9 Im (ref_31) 2009; 46 |
| References_xml | – ident: ref_34 doi: 10.3390/rs13193990 – volume: 103 start-page: 16 year: 2006 ident: ref_3 article-title: Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2006.03.008 – volume: 43 start-page: 3927 year: 2022 ident: ref_39 article-title: Tensor-based keypoint detection and switching regression model for relative radiometric normalization of bitemporal multispectral images publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2022.2102951 – volume: 34 start-page: 98 year: 2019 ident: ref_15 article-title: Relative Radiometric Normalisation-performance testing of selected techniques and impact analysis on vegetation and water bodies publication-title: Geocarto Int. doi: 10.1080/10106049.2017.1367424 – volume: 27 start-page: 5873 year: 2020 ident: ref_1 article-title: Monitoring land cover change detection with NOAA-AVHRR and MODIS remotely sensed data in the North and West of Africa from 1982 to 2015 publication-title: Environ. Sci. Pollut. Res. doi: 10.1007/s11356-019-07216-1 – ident: ref_35 doi: 10.1007/978-3-662-03978-6 – volume: 37 start-page: 4554 year: 2016 ident: ref_24 article-title: A new model for the automatic relative radiometric normalization of multiple images with pseudo-invariant features publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2016.1213922 – volume: 16 start-page: 1353 year: 2019 ident: ref_25 article-title: Pseudoinvariant feature selection using multitemporal MAD for optical satellite images publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2019.2899969 – volume: 249 start-page: 105308 year: 2021 ident: ref_12 article-title: Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data publication-title: Atmos. Res. doi: 10.1016/j.atmosres.2020.105308 – volume: 5 start-page: 2763 year: 2013 ident: ref_37 article-title: Radiometric normalization of temporal images combining automatic detection of pseudo-invariant features from the distance and similarity spectral measures, density scatterplot analysis, and robust regression publication-title: Remote Sens. doi: 10.3390/rs5062763 – ident: ref_23 doi: 10.3390/rs9121319 – volume: 147 start-page: 56 year: 2019 ident: ref_22 article-title: Spectral-consistent relative radiometric normalization for multitemporal Landsat 8 imagery publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2018.11.007 – volume: 112 start-page: 1025 year: 2008 ident: ref_28 article-title: Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2007.07.013 – volume: 61 start-page: 1255 year: 1995 ident: ref_29 article-title: Relative radiometric normalization of Landsat multispectral scanner (MSS) data using an automatic scattergram-controlled regression publication-title: Photogramm. Eng. Remote Sens. – volume: 57 start-page: 785 year: 2020 ident: ref_2 article-title: Evaluation of machine learning models for rice dry biomass estimation and mapping using quad-source optical imagery publication-title: GIScience Remote Sens. doi: 10.1080/15481603.2020.1799546 – volume: 195 start-page: 1 year: 2023 ident: ref_16 article-title: Multi-spectral multi-image super-resolution of Sentinel-2 with radiometric consistency losses and its effect on building delineation publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2022.10.019 – volume: 13 start-page: 6029 year: 2020 ident: ref_26 article-title: Robust radiometric normalization of multitemporal satellite images via block adjustment without master images publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2020.3028062 – ident: ref_13 doi: 10.3390/rs8050411 – volume: 49 start-page: 755 year: 2012 ident: ref_38 article-title: Automatic pseudo-invariant feature extraction for the relative radiometric normalization of hyperion hyperspectral images publication-title: GIScience Remote Sens. doi: 10.2747/1548-1603.49.5.755 – volume: 14 start-page: 4063 year: 2021 ident: ref_18 article-title: Comparison of Keypoint Detectors and Descriptors for Relative Radiometric Normalization of Bitemporal Remote Sensing Images publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2021.3069919 – volume: 37 start-page: 6437 year: 2013 ident: ref_19 article-title: A new model for automatic normalization of multitemporal satellite images using Artificial Neural Network and mathematical methods publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2013.01.006 – volume: 35 start-page: 11 year: 1991 ident: ref_20 article-title: Radiometric rectification: Toward a common radiometric response among multidate, multisensor images publication-title: Remote Sens. Environ. doi: 10.1016/0034-4257(91)90062-B – ident: ref_8 doi: 10.3390/s18124505 – volume: 36 start-page: 825 year: 2017 ident: ref_21 article-title: A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery publication-title: Comput. Appl. Math. doi: 10.1007/s40314-015-0254-z – volume: 29 start-page: 425 year: 2008 ident: ref_32 article-title: A comparative study on radiometric normalization using high resolution satellite images publication-title: Int. J. Remote Sens. doi: 10.1080/01431160601086019 – ident: ref_9 doi: 10.3390/rs14081777 – volume: 6 start-page: 235 year: 2005 ident: ref_30 article-title: Automatic normalization of satellite images using unchanged pixels within urban areas publication-title: Inf. Fusion doi: 10.1016/j.inffus.2004.12.002 – ident: ref_17 doi: 10.3390/rs13163125 – volume: 164 start-page: 104893 year: 2019 ident: ref_14 article-title: Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2019.104893 – volume: 53 start-page: 126675 year: 2020 ident: ref_5 article-title: Mapping urban tree species by integrating multi-seasonal high resolution pléiades satellite imagery with airborne LiDAR data publication-title: Urban For. Urban Green. doi: 10.1016/j.ufug.2020.126675 – volume: 60 start-page: 5400820 year: 2022 ident: ref_33 article-title: Distortion Robust Relative Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Using Image Features publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2021.3063151 – volume: 6 start-page: 11810 year: 2014 ident: ref_36 article-title: An assessment of polynomial regression techniques for the relative radiometric normalization (RRN) of high-resolution multi-temporal airborne thermal infrared (TIR) imagery publication-title: Remote Sens. doi: 10.3390/rs61211810 – volume: 79 start-page: 471 year: 2020 ident: ref_6 article-title: Assessment of human-induced environmental disaster in the Aral Sea using Landsat satellite images publication-title: Environ. Earth Sci. doi: 10.1007/s12665-020-09220-y – volume: 46 start-page: 249 year: 2009 ident: ref_31 article-title: Characteristics of search spaces for identifying optimum thresholds in change detection studies publication-title: GIScience Remote Sens. doi: 10.2747/1548-1603.46.3.249 – volume: 106 start-page: 82 year: 2015 ident: ref_7 article-title: A comparison of four relative radiometric normalization (RRN) techniques for mosaicing H-res multi-temporal thermal infrared (TIR) flight-lines of a complex urban scene publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2015.05.002 – volume: 19 start-page: 1 year: 2022 ident: ref_10 article-title: Random Sampling-Based Relative Radiometric Normalization publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2020.3047344 – volume: 9 start-page: 649 year: 2016 ident: ref_11 article-title: Radiometric normalization of overlapping LiDAR intensity data for reduction of striping noise publication-title: Int. J. Digit. Earth doi: 10.1080/17538947.2015.1111951 – volume: 42 start-page: 6155 year: 2021 ident: ref_27 article-title: A novel automatic method on pseudo-invariant features extraction for enhancing the relative radiometric normalization of high-resolution images publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2021.1934912 – volume: 82 start-page: 101905 year: 2019 ident: ref_4 article-title: Satellite data integration for soil clay content modelling at a national scale publication-title: Int. J. Appl. Earth Obs. Geoinf. |
| SSID | ssj0000913810 |
| Score | 2.287201 |
| Snippet | As sensor parameters and atmospheric conditions create uncertainties for at−sensor radiation detection, radiometric consistency among satellite images is... |
| SourceID | doaj unpaywall proquest gale crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | 2525 |
| SubjectTerms | Algorithms Analysis contemporaneous satellite images Earth resources technology satellites Libraries multiple−band regression pseudo−invariant polygon features Radiation Regression analysis Remote sensing Sensors single−band regression |
| SummonAdditionalLinks | – databaseName: ProQuest Central (via ProQuest) dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3NbtQwEB6V7QF6QLSACBTkQxE_UkSaH9s5ILSLWnVRWValSL1FdmwvhzRZuhvQvgFnDjwgT8JM1rtdCdSrM4kcz3j8je35BuCAWyc4VzaMbJKFqU7SUMbChFxyBAsyU6ZLpP044idf0g8X2cUWjFa5MHStcuUTO0dtmpL2yN_EQuRZLBAhvJt-C6lqFJ2urkpoKF9awbztKMZuwXZMzFg92B4cjcZn610XYsGUh9EyUS_BeJ_OiQ8ppM-oWPbG0tQx-P_rp3fgdltP1eKHqqqNhej4Htz1CJL1lyrfhS1b78HOBq_gHuz6GTtjLz2t9Kv78PtMGcq1J0p-NiKoWvkcTNbdG2CKjWe2Nc2fn7-G9XeMoXHQ2bipFhMUIajYYmiODwe48BnWryY4PPOvl4y2ctk1y1Vtm3bGPtMtpNpWKB_3maoNO6WkYjXHBsk-nQ7Z8JLoMxYP4Pz46Pz9SeirMoRlGiVzKklXJloJpzNuUaOGzpSlUVGspHNxmSltVBLrBGPLPMlcnGmpjMud5Uagf30Ivbqp7SNgpZVWpiqVSkYpvppryy2GgNYKkTgdBfB6pY-i9IzlVDijKjByIeUVG8oL4GAtPF0SdfxfbECKXYsQu3bX0FxNCj9ZC6s1hpFcukimqRNWoR0bVybc5arEpgBekFkU5AOwQ6XyqQz4W8SmVfQF4mKJUCgPYH9lOYV3DrPi2pQDeL62ppt6_fjmzzyBOzFir-Vl8n3oza9a-xSx0lw_8xPgL8wCGbc priority: 102 providerName: ProQuest – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1NbxMxELVQeoAegBYQgYJ8KOJD2mZjrz_2hFJE1aASImilctraaztUbHejZLco_ALOHPiB_BLGiRMiQAiJq3cs2drx-L3dmTcI7XLrBOfKRrGlLEo0TSJJhIm45AAWJFNmXkj7esAPT5JXp-w09DmdhrRKoOLn8yBNgGRHEGZFp0s7SYcwwjpj455fhk9JXS6AY3HOIAZvcAZgvIU2TgbD3nvfUm45eVGVR4Hc-5_CXc_fme-MvXYPzeX6fw_Km-hqU47V7JMqirVb5-AGOluud5Fs8nGvqfVe_vkXKcf_2NBNdD0gUtxbuNAWumLLbbS5plO4jbZCBJjiJ0Gm-ukt9O2tMr5230v844GHvkWo6cTzPASs8HBqG1N9__K1X14CJ4eXiIdVMRuBiYeeDVB9eLgPF6nBvWJUTc7rDxfYfxrGP1WzSls1U_zOZzWVtgB70sOqNPjIFymrGgYkfnPUx_0LL8cxu42OD14evziMQpeHKE9iWvsWdznVSjjNuAUPMf4ftTQqJko6R3KmtFGUaApcNaXMEaalMi51lhsB8foOapVVae8inFtpZaISqWScwNRUW26BUlorBHU6bqNny1ee5UEB3TfiKDJgQt4_sjX_aKPdlfF4IfzxZ7N97zsrE6_WPR-oJqMsHP7Mag20lEsXyyRxwio4F8bllLtU5TDURo-952U-psCCchVKI2BbXp0r6wnA2RKgVdpGO0vnzEKwmWZEiJQRAeC5jR6tHPZvq773j3b30TUCoG6Rpb6DWvWksQ8AhNX6YThoPwCbIjEW priority: 102 providerName: Unpaywall |
| Title | Radiometric Normalization Using a Pseudo−Invariant Polygon Features−Based Algorithm with Contemporaneous Sentinel−2A and Landsat−8 OLI Imagery |
| URI | https://www.proquest.com/docview/2779527085 https://www.mdpi.com/2076-3417/13/4/2525/pdf?version=1677136652 https://doaj.org/article/ebb71668f0844f7eae35dfc36f9ac844 |
| UnpaywallVersion | publishedVersion |
| Volume | 13 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAFT databaseName: Colorado Digital library customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: KQ8 dateStart: 20110101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: Openly Available Collection - DOAJ customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: ADMLS dateStart: 20120901 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 2076-3417 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: 8FG dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NbtQwEB5BOUAPiBYQgbLyoYgfKSLNj-0cs6hLF5VlVVqpnCI7tgtSmlTdLGjfgDMHHpAnYSZJl0gguHCMM4kcz3j8Tez5BmCXWyc4V9YPbJT4sY5iX4bC-FxyBAsyUaZNpH074wcn8ZvT5HRQ6ovOhHX0wN3AvbRaI6Tn0gUyjp2wCt9pXBFxl6oCm8j7BjIdBFOtD073iLqqS8iLMK6n_eA9Ct0TKoo9WIJapv7f_fEm3FxWF2r1RZXlYMGZ3IHbPVJkWdfDLbhmq23YHPAHbsNWPzMX7FlPH_38Lnw_UoZy6ol6n80IkpZ9riVrzwcwxeYLuzT1j6_fptVnjJVxcNm8LldnKEKQcIkhON4c4wJnWFae1Zefmo_njH7Zsl9sVpWtlwv2nk4bVbZE-TBjqjLskJKHVYMNkr07nLLpOdFkrO7B8WT_-NWB31df8Is4iBoqPVdEWgmnE25Rc4b2jqVRQaikc2GRKG1UFOoIY8g0SlyYaKmMS53lRqAfvQ8bVV3ZB8AKK62MVSyVDGJ8NNWWWwz1rBUicjrw4MWVPvKiZyanAhlljhEKKS8fKM-D3bXwRUfI8WexMSl2LUIs2m0D2lbe21b-L9vy4CmZRU5zHTtUqD5lAT-LWLPyTCD-lQh5Ug92riwn753AIg-FSJNQIKj14Mnamv7W64f_o9eP4FaISKw7Wr4DG83l0j5G5NToEVyXk9cjuDHen82PRu2UwauT2Tz78BOj8yDO |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3NbhMxELaq9lB6QLSASCngQyt-pBUbe3-8hwol0CqhaYhKkHqzvGs7HLa7IZtQ5Q04c-BteBmehJmNk0YC9dard3bl1djzY898HyGHkbFxFCnj-YaHXpDywBMs1l4kIggWRKh03Uh73o86X4KPl-HlBvm97IXBssqlTawNtS4zPCN_y-I4CVkMEcK78TcPWaPwdnVJoaEctYI-riHGXGPHmZlfQwpXHXc_gL6PGDs9Gb7veI5lwMsCn0-RYi3jqYptGkYGZqjxjlRo5TMlrGVZqFKtOEs55EoJDy0LU6G0TayJdNxE0gjwAFsBh-9skq32SX9wsTrkQdBN0fQXfYGcJz5eSzfxBCFEbu41T1gTBvzrFnbI9qwYq_m1yvM1v3f6gNx3ASttLVbYLtkwxR7ZWYMx3CO7zkBU9JVDsX79kPy6UBpb-5EBgPYxMs5dyyetyxSoooPKzHT558fPbvEdUnbQMR2U-XwEIhiZziamgodt8LOatvIRaGP69YriyTG9AdUqTDmr6GcseipMDvKsRVWhaQ97mNUUBgT91OvS7hWidcwfkeFdqOcx2SzKwjwhNDPCiEAFQgk_gFeT1EQGMk5j4pjb1G-QN0t9yMwBpCNPRy4hUULlyTXlNcjhSni8wAX5v1gbFbsSQTDveqCcjKSzDdKkKWStkbC-CAIbGwXbRtuMRzZRGQw1yEtcFhJNDkwoU65zAn4LwbtkK4YwXEDklTTIwXLlSGeLKnmzcxrkaLWabpv1_u2feUG2O8Pznux1-2dPyT0GYd-ijv2AbE4nM_MMwrRp-txtBkrkHW-_vzVXVsM |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3NbhMxELaqIgE9IFpABAr40IofadWNvWt7DwillNDQECJopd5W9toOh-1u6CZUeQPOHHgXXocnYWazSSOBeuvVO4m8Oz-eGc98Q8iOcF4KoV0QOh4HkeFRoJi0gVACnAUVa1s30n4ciMOT6MNpfLpGfi96YbCscmETa0Ntywxz5HtMyiRmEjyEPd-URQwPum_G3wKcIIU3rYtxGnMROXKzCwjfqte9A-D1LmPdd8dvD4NmwkCQRSGf4Hi1jBstvYmFg91ZvB9VVodMK-9ZFmtjNWeGQ5yU8Niz2ChtfeKdsLKNAyPA-t-QCOKOTerd98v0DsJtqnY47wjkPAnxQrqNuYMYp3KvnIH1qIB_D4QNcmtajPXsQuf5yonXvUvuNK4q7cxla5OsuWKLbKwAGG6RzcY0VPRFg1_98h759VlbbOpH7H86QJ84b5o9aV2gQDUdVm5qyz8_fvaK7xCsA3fpsMxnIyBBn3R67ip4uA8nrKWdfATffvL1jGLOmF7CaRWunFb0C5Y7FS4HetahurC0j93LegILin7q92jvDHE6ZvfJ8XUw5wFZL8rCPSQ0c8qpSEdKqzCCnybGCQexpnNScm_CFnm14EeaNdDoOKEjTyFEQualK8xrkZ0l8XiOCPJ_sn1k7JIEYbzrhfJ8lDZWIXXGQLwqlA9VFHnpNCiM9RkXPtEZLLXIcxSLFI0NbCjTTc8EvBbCdqUdCQ64Ap8raZHtheSkjRWq0kudaZHdpTRdtetHV__NM3ITlC7t9wZHj8ltBv7evIB9m6xPzqfuCfhnE_O01gRK0mvWvL_3AVRd |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1NbxMxELVQeoAegBYQgYJ8KOJD2mZjrz_2hFJE1aASImilctraaztUbHejZLco_ALOHPiB_BLGiRMiQAiJq3cs2drx-L3dmTcI7XLrBOfKRrGlLEo0TSJJhIm45AAWJFNmXkj7esAPT5JXp-w09DmdhrRKoOLn8yBNgGRHEGZFp0s7SYcwwjpj455fhk9JXS6AY3HOIAZvcAZgvIU2TgbD3nvfUm45eVGVR4Hc-5_CXc_fme-MvXYPzeX6fw_Km-hqU47V7JMqirVb5-AGOluud5Fs8nGvqfVe_vkXKcf_2NBNdD0gUtxbuNAWumLLbbS5plO4jbZCBJjiJ0Gm-ukt9O2tMr5230v844GHvkWo6cTzPASs8HBqG1N9__K1X14CJ4eXiIdVMRuBiYeeDVB9eLgPF6nBvWJUTc7rDxfYfxrGP1WzSls1U_zOZzWVtgB70sOqNPjIFymrGgYkfnPUx_0LL8cxu42OD14evziMQpeHKE9iWvsWdznVSjjNuAUPMf4ftTQqJko6R3KmtFGUaApcNaXMEaalMi51lhsB8foOapVVae8inFtpZaISqWScwNRUW26BUlorBHU6bqNny1ee5UEB3TfiKDJgQt4_sjX_aKPdlfF4IfzxZ7N97zsrE6_WPR-oJqMsHP7Mag20lEsXyyRxwio4F8bllLtU5TDURo-952U-psCCchVKI2BbXp0r6wnA2RKgVdpGO0vnzEKwmWZEiJQRAeC5jR6tHPZvq773j3b30TUCoG6Rpb6DWvWksQ8AhNX6YThoPwCbIjEW |
| 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=Radiometric+Normalization+Using+a+Pseudo%E2%88%92Invariant+Polygon+Features%E2%88%92Based+Algorithm+with+Contemporaneous+Sentinel%E2%88%922A+and+Landsat%E2%88%928+OLI+Imagery&rft.jtitle=Applied+sciences&rft.au=Lei+Chen&rft.au=Ying+Ma&rft.au=Yi+Lian&rft.au=Hu+Zhang&rft.date=2023-02-01&rft.pub=MDPI+AG&rft.eissn=2076-3417&rft.volume=13&rft.issue=4&rft.spage=2525&rft_id=info:doi/10.3390%2Fapp13042525&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_ebb71668f0844f7eae35dfc36f9ac844 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2076-3417&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2076-3417&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2076-3417&client=summon |