Thick cloud and cloud shadow removal in multitemporal imagery using progressively spatio-temporal patch group deep learning
Thick cloud and its shadow severely reduce the data usability of optical satellite remote sensing data. Although many approaches have been presented for cloud and cloud shadow removal, most of these approaches are still inadequate in terms of dealing with the following three issues: (1) thick cloud...
Saved in:
| Published in | ISPRS journal of photogrammetry and remote sensing Vol. 162; pp. 148 - 160 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.04.2020
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Thick cloud and its shadow severely reduce the data usability of optical satellite remote sensing data. Although many approaches have been presented for cloud and cloud shadow removal, most of these approaches are still inadequate in terms of dealing with the following three issues: (1) thick cloud cover with large-scale areas, (2) all the temporal images included cloud or shadow, and (3) deficient utilization of only single temporal images. A novel spatio-temporal patch group deep learning framework for gap-filling through multiple temporal cloudy images is proposed to overcome these issues. The global-local loss function is presented to optimize the training model through cloud-covered and free regions, considering both the global consistency and local particularity. In addition, weighted aggregation and progressive iteration are utilized for reconstructing the holistic results. A series of simulated and real experiments are then performed to validate the effectiveness of the proposed method. Especially on Sentinel-2 MSI and Landsat-8 OLI with single/multitemporal images, under small/large scale regions, respectively. |
|---|---|
| AbstractList | Thick cloud and its shadow severely reduce the data usability of optical satellite remote sensing data. Although many approaches have been presented for cloud and cloud shadow removal, most of these approaches are still inadequate in terms of dealing with the following three issues: (1) thick cloud cover with large-scale areas, (2) all the temporal images included cloud or shadow, and (3) deficient utilization of only single temporal images. A novel spatio-temporal patch group deep learning framework for gap-filling through multiple temporal cloudy images is proposed to overcome these issues. The global-local loss function is presented to optimize the training model through cloud-covered and free regions, considering both the global consistency and local particularity. In addition, weighted aggregation and progressive iteration are utilized for reconstructing the holistic results. A series of simulated and real experiments are then performed to validate the effectiveness of the proposed method. Especially on Sentinel-2 MSI and Landsat-8 OLI with single/multitemporal images, under small/large scale regions, respectively. |
| Author | Yuan, Qiangqiang Li, Zhiwei Shen, Huanfeng Zhang, Liangpei Zhang, Qiang Li, Jie |
| Author_xml | – sequence: 1 givenname: Qiang orcidid: 0000-0002-7116-9327 surname: Zhang fullname: Zhang, Qiang organization: State Key Laboratory of Information Engineering, Survey Mapping and Remote Sensing, Wuhan University, Wuhan, China – sequence: 2 givenname: Qiangqiang orcidid: 0000-0001-7140-2224 surname: Yuan fullname: Yuan, Qiangqiang email: yqiang86@gmail.com organization: School of Geodesy and Geomatics, Wuhan University, Wuhan, China – sequence: 3 givenname: Jie surname: Li fullname: Li, Jie organization: School of Geodesy and Geomatics, Wuhan University, Wuhan, China – sequence: 4 givenname: Zhiwei orcidid: 0000-0001-5635-8499 surname: Li fullname: Li, Zhiwei organization: School of Resource and Environmental Science, Wuhan University, Wuhan, China – sequence: 5 givenname: Huanfeng orcidid: 0000-0002-4140-1869 surname: Shen fullname: Shen, Huanfeng organization: School of Resource and Environmental Science, Wuhan University, Wuhan, China – sequence: 6 givenname: Liangpei orcidid: 0000-0001-6890-3650 surname: Zhang fullname: Zhang, Liangpei email: zlp62@whu.edu.cn organization: State Key Laboratory of Information Engineering, Survey Mapping and Remote Sensing, Wuhan University, Wuhan, China |
| BookMark | eNqNkL1uHCEURpHlSF47eYZQppnxBQ8MW6SwrCSOZCmNUyMMd3bZMDCBmY1WeXmzWstFmrhA_Og7ny7nkpzHFJGQjwxaBkxe71pfplx2dbUcOLTAWwB1RlZM9bxR_EackxWsedfwnskLclnKDgCYkGpF_j5uvf1FbUiLoya6l1PZGpf-0Ixj2ptAfaTjEmY_4zilfHwYzQbzgS7Fxw2dctpkLMXvMRxomczsU_OarVe7pZuclok6xIkGNDlW7j15N5hQ8MPLfkV-fv3yeHffPPz49v3u9qGxHevmprPCimFwUnHpnpwC7Jx0yhlYD8JwIZ1VglkJa3wSSq0BhetB9IjSOgndzRX5dOqtc_5esMx69MViCCZiWormHYCoPUrU6OdT1OZUSsZBWz8fvxPnbHzQDPRRut7pV-n6KF0D11V65ft_-ClXV_nwBvL2RGI1sfeYdbEeo0XnM9pZu-T_2_EMMFSonQ |
| CitedBy_id | crossref_primary_10_1109_TGRS_2023_3326545 crossref_primary_10_3390_rs15020299 crossref_primary_10_1016_j_engappai_2025_110508 crossref_primary_10_1016_j_isprsjprs_2025_01_016 crossref_primary_10_3390_agronomy13112789 crossref_primary_10_1016_j_isprsjprs_2025_01_013 crossref_primary_10_1109_TGRS_2024_3427788 crossref_primary_10_3390_rs13234736 crossref_primary_10_1016_j_isprsjprs_2023_01_004 crossref_primary_10_1109_TGRS_2023_3318521 crossref_primary_10_3390_rs16071222 crossref_primary_10_1016_j_isprsjprs_2022_03_013 crossref_primary_10_1109_TGRS_2022_3152630 crossref_primary_10_1109_LGRS_2021_3112661 crossref_primary_10_3390_rs16173134 crossref_primary_10_1016_j_isprsjprs_2024_07_030 crossref_primary_10_3390_rs16091518 crossref_primary_10_1109_TCSVT_2023_3316279 crossref_primary_10_1016_j_jhydrol_2023_129561 crossref_primary_10_1016_j_isprsjprs_2024_01_007 crossref_primary_10_1016_j_neucom_2025_129749 crossref_primary_10_3390_rs14010172 crossref_primary_10_1109_MGRS_2022_3165967 crossref_primary_10_1109_TGRS_2023_3339210 crossref_primary_10_1109_TGRS_2021_3121272 crossref_primary_10_1016_j_isprsjprs_2020_08_016 crossref_primary_10_1080_10095020_2023_2189462 crossref_primary_10_2174_2210298102666220616114622 crossref_primary_10_1016_j_isprsjprs_2024_06_010 crossref_primary_10_1016_j_rse_2023_113928 crossref_primary_10_1080_01431161_2021_2012295 crossref_primary_10_1109_ACCESS_2022_3213354 crossref_primary_10_1109_TGRS_2022_3186916 crossref_primary_10_1016_j_jag_2022_102773 crossref_primary_10_1109_TGRS_2021_3101848 crossref_primary_10_1109_JSTARS_2020_3038778 crossref_primary_10_3390_rs15082119 crossref_primary_10_1016_j_neucom_2024_128963 crossref_primary_10_1109_TCSVT_2024_3453393 crossref_primary_10_1016_j_isprsjprs_2023_04_006 crossref_primary_10_1109_TNNLS_2023_3278866 crossref_primary_10_1016_j_jag_2022_103079 crossref_primary_10_3390_rs16081392 crossref_primary_10_1109_JSTARS_2021_3075727 crossref_primary_10_1080_01431161_2021_1949069 crossref_primary_10_1109_TGRS_2024_3485595 crossref_primary_10_1155_2020_8811630 crossref_primary_10_1109_TGRS_2023_3347930 crossref_primary_10_1109_TGRS_2024_3458174 crossref_primary_10_3390_rs14194692 crossref_primary_10_1016_j_isprsjprs_2020_04_010 crossref_primary_10_1109_LGRS_2021_3069895 crossref_primary_10_1016_j_jag_2021_102640 crossref_primary_10_1016_j_jhydrol_2022_128791 crossref_primary_10_5194_essd_14_4473_2022 crossref_primary_10_1016_j_jag_2024_104244 crossref_primary_10_1080_15481603_2022_2160411 crossref_primary_10_3390_rs15215145 crossref_primary_10_1016_j_sigpro_2023_109305 crossref_primary_10_1109_TIP_2022_3211471 crossref_primary_10_1016_j_srs_2023_100102 crossref_primary_10_1016_j_isprsjprs_2021_08_013 crossref_primary_10_1016_j_srs_2022_100047 crossref_primary_10_3390_rs17061022 crossref_primary_10_1109_TGRS_2021_3076667 crossref_primary_10_1080_01431161_2023_2292014 crossref_primary_10_1080_10106049_2021_1892206 crossref_primary_10_1016_j_jag_2021_102470 crossref_primary_10_3390_rs13142709 crossref_primary_10_1016_j_isprsjprs_2021_04_021 crossref_primary_10_1080_2150704X_2022_2152753 crossref_primary_10_1109_TGRS_2025_3543617 crossref_primary_10_1109_TGRS_2022_3208694 crossref_primary_10_1109_JSTARS_2022_3229729 crossref_primary_10_1109_TGRS_2021_3131035 crossref_primary_10_1109_ACCESS_2020_3024164 crossref_primary_10_3390_rs14153648 crossref_primary_10_1109_TGRS_2024_3431556 crossref_primary_10_3390_rs16010152 crossref_primary_10_1080_01431161_2024_2433755 crossref_primary_10_1109_JSTARS_2024_3492177 crossref_primary_10_3390_rs14133134 crossref_primary_10_3390_rs12203427 crossref_primary_10_1109_TGRS_2023_3333391 crossref_primary_10_1016_j_compeleceng_2022_108177 crossref_primary_10_1016_j_rse_2024_114360 crossref_primary_10_1080_01431161_2020_1841324 crossref_primary_10_1109_TGRS_2024_3358493 crossref_primary_10_1109_TGRS_2024_3451558 crossref_primary_10_1080_15481603_2023_2196133 crossref_primary_10_1080_15481603_2024_2421574 crossref_primary_10_1016_j_engappai_2024_108861 crossref_primary_10_3390_rs16132293 crossref_primary_10_1016_j_advengsoft_2024_103856 crossref_primary_10_1016_j_imavis_2024_105100 crossref_primary_10_1016_j_rse_2024_114245 crossref_primary_10_1109_TGRS_2023_3269622 crossref_primary_10_1109_TGRS_2023_3303169 crossref_primary_10_3390_rs14174252 crossref_primary_10_3390_rs15041137 crossref_primary_10_1016_j_jag_2023_103506 crossref_primary_10_3390_rs16122150 crossref_primary_10_1016_j_jhydrol_2024_131673 crossref_primary_10_1016_j_jag_2022_102731 crossref_primary_10_1016_j_sigpro_2022_108888 crossref_primary_10_1109_TGRS_2024_3472645 crossref_primary_10_1049_cit2_12373 crossref_primary_10_22630_MGV_2023_32_2_8 crossref_primary_10_1155_2023_9953198 crossref_primary_10_5194_essd_13_1385_2021 crossref_primary_10_1109_JSTARS_2023_3235940 crossref_primary_10_1109_TGRS_2024_3468718 crossref_primary_10_1109_LGRS_2021_3093887 crossref_primary_10_1109_TGRS_2023_3291822 crossref_primary_10_1080_15481603_2022_2031549 crossref_primary_10_1109_TGRS_2022_3140800 crossref_primary_10_1109_LGRS_2024_3397875 crossref_primary_10_1109_JSTARS_2022_3203508 crossref_primary_10_1016_j_isprsjprs_2023_03_003 crossref_primary_10_3390_rs13234947 crossref_primary_10_1109_TGRS_2023_3236106 crossref_primary_10_1016_j_rse_2024_114142 crossref_primary_10_1016_j_rse_2022_113132 crossref_primary_10_1109_TGRS_2021_3128280 crossref_primary_10_1016_j_rse_2024_114546 crossref_primary_10_1109_TGRS_2024_3416128 crossref_primary_10_3390_app13169296 crossref_primary_10_1016_j_rse_2022_113136 crossref_primary_10_3390_agronomy14061089 |
| Cites_doi | 10.1016/j.isprsjprs.2014.02.015 10.1016/j.rse.2012.12.012 10.1016/j.isprsjprs.2015.03.009 10.1109/TGRS.2018.2810208 10.1109/MSP.2013.2273004 10.1016/j.isprsjprs.2019.09.003 10.1016/j.isprsjprs.2019.01.025 10.3390/rs10020196 10.1109/TGRS.2018.2865197 10.1109/LGRS.2011.2173290 10.1016/j.rse.2013.09.002 10.1109/TGRS.2014.2307354 10.1016/j.rse.2015.12.008 10.1016/j.rse.2017.07.002 10.1016/j.rse.2011.10.028 10.1080/01431161.2012.666363 10.1109/TPAMI.2014.2330611 10.1016/j.rse.2018.11.032 10.1109/TIP.2003.815261 10.1109/MGRS.2015.2441912 10.1016/j.isprsjprs.2014.06.011 10.1109/TGRS.2017.2656162 10.3390/rs11050523 10.1109/LGRS.2017.2736020 10.1016/j.isprsjprs.2018.12.013 10.1109/JSTARS.2018.2794888 10.1109/TPAMI.2015.2439281 10.1016/j.rse.2016.03.034 10.1016/j.isprsjprs.2019.03.012 10.1109/TGRS.2019.2958096 10.1016/j.rse.2017.01.026 10.1109/TGRS.2017.2670021 10.1109/TGRS.2008.2003436 10.1016/0034-4257(94)90057-4 10.3390/rs11040433 10.1016/j.rse.2018.03.023 10.1016/j.isprsjprs.2019.08.017 10.1109/TGRS.2015.2509860 10.1109/TGRS.2016.2580576 10.1016/j.rse.2010.12.010 10.1109/TIP.2004.833105 10.1016/j.rse.2018.07.006 10.1109/TGRS.2019.2912909 10.1109/TGRS.2018.2790262 10.3390/rs11161925 10.1016/j.isprsjprs.2019.02.017 10.1038/nature14539 10.1109/TIP.2017.2662206 10.1016/j.jag.2009.11.002 10.1016/j.rse.2014.12.014 |
| ContentType | Journal Article |
| Copyright | 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) |
| Copyright_xml | – notice: 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) |
| DBID | AAYXX CITATION 7S9 L.6 |
| DOI | 10.1016/j.isprsjprs.2020.02.008 |
| DatabaseName | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geography Engineering |
| EISSN | 1872-8235 |
| EndPage | 160 |
| ExternalDocumentID | 10_1016_j_isprsjprs_2020_02_008 S0924271620300423 |
| GroupedDBID | --K --M .~1 0R~ 1B1 1RT 1~. 1~5 29J 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABJNI ABMAC ABQEM ABQYD ABXDB ABYKQ ACDAQ ACGFS ACLVX ACNNM ACRLP ACSBN ACZNC ADBBV ADEZE ADJOM ADMUD AEBSH AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG ATOGT AVWKF AXJTR AZFZN BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q G8K GBLVA GBOLZ HMA HVGLF HZ~ H~9 IHE IMUCA J1W KOM LY3 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG RNS ROL RPZ SDF SDG SEP SES SEW SPC SPCBC SSE SSV SSZ T5K T9H WUQ ZMT ~02 ~G- AAHBH AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AFXIZ AGCQF AGQPQ AGRNS AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP BNPGV CITATION SSH 7S9 EFKBS L.6 |
| ID | FETCH-LOGICAL-c414t-4c5c5ffd6826dbd80e4d6d8da09f5a256dc851c609eb58890e5d7057ee6cd6043 |
| IEDL.DBID | .~1 |
| ISSN | 0924-2716 |
| IngestDate | Mon Aug 18 09:43:23 EDT 2025 Thu Apr 24 23:01:41 EDT 2025 Tue Jul 01 03:46:43 EDT 2025 Fri Feb 23 02:47:45 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Spatio-temporal Patch group Global-local CNN Progressive iteration Thick cloud and cloud shadow Gap-filling |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c414t-4c5c5ffd6826dbd80e4d6d8da09f5a256dc851c609eb58890e5d7057ee6cd6043 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-4140-1869 0000-0001-6890-3650 0000-0001-5635-8499 0000-0002-7116-9327 0000-0001-7140-2224 |
| PQID | 2400525685 |
| PQPubID | 24069 |
| PageCount | 13 |
| ParticipantIDs | proquest_miscellaneous_2400525685 crossref_citationtrail_10_1016_j_isprsjprs_2020_02_008 crossref_primary_10_1016_j_isprsjprs_2020_02_008 elsevier_sciencedirect_doi_10_1016_j_isprsjprs_2020_02_008 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | April 2020 2020-04-00 20200401 |
| PublicationDateYYYYMMDD | 2020-04-01 |
| PublicationDate_xml | – month: 04 year: 2020 text: April 2020 |
| PublicationDecade | 2020 |
| PublicationTitle | ISPRS journal of photogrammetry and remote sensing |
| PublicationYear | 2020 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Pelletier, Webb, Petitjean (b0145) 2019; 11 Rossi, Dungan, Beck (b0160) 1994; 49 Zhu, Gao, Liu, Chen (b0270) 2012; 9 Peng, Chen, Lü, Liu, Wu (b0150) 2016; 174 Weng, Fu (b0210) 2014; 140 LeCun, Bengio, Hinton (b0095) 2015; 521 Xu, Jia, Pickering, Jia (b0220) 2019; 149 Zhu, Wang, Woodcock (b0280) 2015; 159 Zhang, Yuan, Li, Yang, Ma (b0250) 2018; 10 Chen, Jin, Brown (b0025) 2019; 151 Li, Shen, Cheng, Li, Zhang (b0120) 2019; 11 Chen, Zhu, Vogelmann, Gao, Jin (b0035) 2011; 115 Qiu, He, Zhu, Liao, Quan (b0185) 2017; 199 Shen, Li, Qian, Zhang, Yuan (b0170) 2014; 96 Lv, Wang, Shen (b0130) 2016; 179 Shen, Li, Cheng, Zeng, Yang, Li, Zhang (b0175) 2015; 3 Ji, Yokoya, Zhu, Huang (b0080) 2018; 56 Chen, He, Yokoya, Huang (b0030) 2019; 157 Ng, Yuan, Yan, Sun (b0135) 2017; 55 Kingma, D.P., Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. Zhang, Wen, Gao, Ling (b0260) 2019 Dong, Loy, He, Tang (b0055) 2016; 38 Shen, Jiang, Li, Yuan, Wei, Zhang (b0165) 2019; 57 Li, Shen, Zhang, Zhang, Yuan, Yang (b0105) 2014; 52 Rakwatin, Takeuchi, Yasuoka (b0155) 2009; 47 Toure, Stow, Shih, Weeks, Lopez-Carr (b0180) 2018; 210 Zhang, Yuan, Li, Liu, Shen, Zhang (b0245) 2019; 57 Cheng, Shen, Zhang, Yuan, Zeng (b0040) 2014; 92 Li, Shen, Li, Xia, Gamba, Zhang (b0125) 2017; 191 Wang, Olsen, Conn, Lozano (b0195) 2016 Guillemot, Olivier (b0070) 2014; 31 Xu, Jia, Pickering, Plaza (b0215) 2016; 54 Zhang, Zuo, Chen, Meng, Zhang (b0240) 2017; 26 Otukei, Blaschke (b0140) 2010; 12 Chan (b0015) 2001; 62 Di Mauro, N., Vergari, A., Basile, T.M.A., Ventola, F.G., Esposito, F., 2017. End-to-end learning of deep spatio-temporal representations for satellite image time series classification. In DC@ PKDD/ECML. Yuan, Wei, Meng, Shen, Zhang (b0225) 2018; 11 Yuan, Zhang, Li, Shen, Zhang (b0230) 2019; 57 He, Sun (b0075) 2014; 36 Chen, Huang, Chen, Xu (b0020) 2017; 55 Wei, Yuan, Shen, Zhang (b0205) 2017; 14 Erinjery, Singh, Kent (b0060) 2018; 216 Zhu, Woodcock (b0275) 2012; 118 Zhang, Yuan, Zeng, Li, Wei (b0255) 2018; 56 Criminisi, Pérez, Toyama (b0045) 2004; 13 Li, Shen, Zhang, Li (b0100) 2015; 106 Van der Meer (b0190) 2012; 33 Bertalmio, Vese, Sapiro, Osher (b0010) 2003; 12 Li, Wang, Cheng, Wu, Gan, Fang (b0110) 2019; 148 Baetens, Desjardins, Hagolle (b0005) 2019; 11 Wang, Yuan, Li, Shen, Zheng, Zhang (b0200) 2019; 157 Li, Shen, Cheng, Liu, You, He (b0115) 2019; 150 Zeng, Shen, Zhang (b0235) 2013; 131 Zhong, Hu, Zhou (b0265) 2019; 221 Gao, Gu (b0065) 2017; 55 Jia, Shelhamer, Donahue (b0085) 2014 Zhu (10.1016/j.isprsjprs.2020.02.008_b0275) 2012; 118 LeCun (10.1016/j.isprsjprs.2020.02.008_b0095) 2015; 521 Van der Meer (10.1016/j.isprsjprs.2020.02.008_b0190) 2012; 33 Toure (10.1016/j.isprsjprs.2020.02.008_b0180) 2018; 210 Yuan (10.1016/j.isprsjprs.2020.02.008_b0230) 2019; 57 Zhang (10.1016/j.isprsjprs.2020.02.008_b0260) 2019 Zhang (10.1016/j.isprsjprs.2020.02.008_b0255) 2018; 56 Cheng (10.1016/j.isprsjprs.2020.02.008_b0040) 2014; 92 Li (10.1016/j.isprsjprs.2020.02.008_b0100) 2015; 106 Chen (10.1016/j.isprsjprs.2020.02.008_b0025) 2019; 151 Chen (10.1016/j.isprsjprs.2020.02.008_b0020) 2017; 55 Guillemot (10.1016/j.isprsjprs.2020.02.008_b0070) 2014; 31 Yuan (10.1016/j.isprsjprs.2020.02.008_b0225) 2018; 11 Zeng (10.1016/j.isprsjprs.2020.02.008_b0235) 2013; 131 Zhu (10.1016/j.isprsjprs.2020.02.008_b0270) 2012; 9 Peng (10.1016/j.isprsjprs.2020.02.008_b0150) 2016; 174 Dong (10.1016/j.isprsjprs.2020.02.008_b0055) 2016; 38 Li (10.1016/j.isprsjprs.2020.02.008_b0120) 2019; 11 Weng (10.1016/j.isprsjprs.2020.02.008_b0210) 2014; 140 Xu (10.1016/j.isprsjprs.2020.02.008_b0215) 2016; 54 Zhu (10.1016/j.isprsjprs.2020.02.008_b0280) 2015; 159 Zhang (10.1016/j.isprsjprs.2020.02.008_b0250) 2018; 10 Erinjery (10.1016/j.isprsjprs.2020.02.008_b0060) 2018; 216 He (10.1016/j.isprsjprs.2020.02.008_b0075) 2014; 36 Zhang (10.1016/j.isprsjprs.2020.02.008_b0240) 2017; 26 Otukei (10.1016/j.isprsjprs.2020.02.008_b0140) 2010; 12 Chen (10.1016/j.isprsjprs.2020.02.008_b0030) 2019; 157 Criminisi (10.1016/j.isprsjprs.2020.02.008_b0045) 2004; 13 Bertalmio (10.1016/j.isprsjprs.2020.02.008_b0010) 2003; 12 Rossi (10.1016/j.isprsjprs.2020.02.008_b0160) 1994; 49 Li (10.1016/j.isprsjprs.2020.02.008_b0110) 2019; 148 Shen (10.1016/j.isprsjprs.2020.02.008_b0170) 2014; 96 Xu (10.1016/j.isprsjprs.2020.02.008_b0220) 2019; 149 10.1016/j.isprsjprs.2020.02.008_b0090 Shen (10.1016/j.isprsjprs.2020.02.008_b0175) 2015; 3 Qiu (10.1016/j.isprsjprs.2020.02.008_b0185) 2017; 199 10.1016/j.isprsjprs.2020.02.008_b0050 Chan (10.1016/j.isprsjprs.2020.02.008_b0015) 2001; 62 Wei (10.1016/j.isprsjprs.2020.02.008_b0205) 2017; 14 Li (10.1016/j.isprsjprs.2020.02.008_b0115) 2019; 150 Pelletier (10.1016/j.isprsjprs.2020.02.008_b0145) 2019; 11 Wang (10.1016/j.isprsjprs.2020.02.008_b0200) 2019; 157 Li (10.1016/j.isprsjprs.2020.02.008_b0125) 2017; 191 Baetens (10.1016/j.isprsjprs.2020.02.008_b0005) 2019; 11 Zhang (10.1016/j.isprsjprs.2020.02.008_b0245) 2019; 57 Li (10.1016/j.isprsjprs.2020.02.008_b0105) 2014; 52 Shen (10.1016/j.isprsjprs.2020.02.008_b0165) 2019; 57 Chen (10.1016/j.isprsjprs.2020.02.008_b0035) 2011; 115 Lv (10.1016/j.isprsjprs.2020.02.008_b0130) 2016; 179 Wang (10.1016/j.isprsjprs.2020.02.008_b0195) 2016 Ng (10.1016/j.isprsjprs.2020.02.008_b0135) 2017; 55 Rakwatin (10.1016/j.isprsjprs.2020.02.008_b0155) 2009; 47 Gao (10.1016/j.isprsjprs.2020.02.008_b0065) 2017; 55 Zhong (10.1016/j.isprsjprs.2020.02.008_b0265) 2019; 221 Ji (10.1016/j.isprsjprs.2020.02.008_b0080) 2018; 56 Jia (10.1016/j.isprsjprs.2020.02.008_b0085) 2014 |
| References_xml | – volume: 38 start-page: 295 year: 2016 end-page: 307 ident: b0055 article-title: Image super-resolution using deep convolutional networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 118 start-page: 83 year: 2012 end-page: 94 ident: b0275 article-title: Object-based cloud and cloud shadow detection in Landsat imagery publication-title: Remote Sens. Environ. – volume: 11 start-page: 978 year: 2018 end-page: 989 ident: b0225 article-title: A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 174 start-page: 109 year: 2016 end-page: 121 ident: b0150 article-title: Spatiotemporal patterns of remotely sensed PM2. 5 concentration in China from 1999 to 2011 publication-title: Remote Sens. Environ. – volume: 151 start-page: 176 year: 2019 end-page: 188 ident: b0025 article-title: Automatic mapping of planting year for tree crops with Landsat satellite time series stacks publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 54 start-page: 2998 year: 2016 end-page: 3006 ident: b0215 article-title: Cloud removal based on sparse representation via multitemporal dictionary learning publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 57 start-page: 7317 year: 2019 end-page: 7329 ident: b0245 article-title: Hybrid noise removal in hyperspectral imagery with a spatial-spectral gradient network publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 11 start-page: 433 year: 2019 ident: b0005 article-title: Validation of copernicus sentinel-2 cloud masks obtained from MAJA, Sen2Cor, and Fmask processors using reference cloud masks generated with a supervised active learning procedure publication-title: Remote Sens. – volume: 115 start-page: 1053 year: 2011 end-page: 1064 ident: b0035 article-title: A simple and effective method for filling gaps in Landsat ETM+ SLC-off images publication-title: Remote Sens. Environ. – start-page: 675 year: 2014 end-page: 678 ident: b0085 article-title: Caffe: Convolutional architecture for fast feature embedding publication-title: Proceedings of the 22nd ACM International Conference on Multimedia – volume: 179 start-page: 183 year: 2016 end-page: 195 ident: b0130 article-title: An empirical and radiative transfer model based algorithm to remove thin clouds in visible bands publication-title: Remote Sens. Environ. – volume: 9 start-page: 521 year: 2012 end-page: 525 ident: b0270 article-title: A modified neighborhood similar pixel interpolator approach for removing thick clouds in Landsat images publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 12 start-page: 27 year: 2010 end-page: 31 ident: b0140 article-title: Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 157 start-page: 1 year: 2019 end-page: 12 ident: b0200 article-title: Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 131 start-page: 182 year: 2013 end-page: 194 ident: b0235 article-title: Recovering missing pixels for Landsat ETM+ SLC-off imagery using multi-temporal regression analysis and a regularization method publication-title: Remote Sens. Environ. – volume: 106 start-page: 1 year: 2015 end-page: 15 ident: b0100 article-title: Sparse-based reconstruction of missing information in remote sensing images from spectral/temporal complementary information publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 56 start-page: 4274 year: 2018 end-page: 4288 ident: b0255 article-title: Missing data reconstruction in remote sensing image with a unified spatial-temporal-spectral deep convolutional neural network publication-title: IEEE Trans. Geosci. Remote Sens. – start-page: 2754 year: 2016 end-page: 2763 ident: b0195 article-title: Removing clouds and recovering ground observations in satellite image sequences via temporally contiguous robust matrix completion publication-title: CVPR – volume: 26 start-page: 3142 year: 2017 end-page: 3155 ident: b0240 article-title: Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising publication-title: IEEE Trans. Image Process. – volume: 55 start-page: 3656 year: 2017 end-page: 3668 ident: b0065 article-title: Multitemporal Landsat missing data recovery based on tempo-spectral angle model publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 150 start-page: 197 year: 2019 end-page: 212 ident: b0115 article-title: Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 3 start-page: 61 year: 2015 end-page: 85 ident: b0175 article-title: Missing information reconstruction of remote sensing data: A technical review publication-title: IEEE Geosci. Remote Sens. Mag. – volume: 199 start-page: 107 year: 2017 end-page: 119 ident: b0185 article-title: Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4–8 images publication-title: Remote Sens. Environ. – volume: 11 start-page: 523 year: 2019 ident: b0145 article-title: Temporal convolutional neural network for the classification of satellite image time series publication-title: Remote Sens. – volume: 62 start-page: 1019 year: 2001 end-page: 1043 ident: b0015 article-title: Local inpainting models and TV inpainting publication-title: SIAM J. Appl. Math. – volume: 57 start-page: 1205 year: 2019 end-page: 1218 ident: b0230 article-title: Hyperspectral Image denoising employing a spatial-spectral deep residual convolutional neural network publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 55 start-page: 27 year: 2017 end-page: 37 ident: b0020 article-title: Spatially and temporally weighted regression: a novel method to produce continuous cloud-free Landsat imagery publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 221 start-page: 430 year: 2019 end-page: 443 ident: b0265 article-title: Deep learning based multi-temporal crop classification publication-title: Remote Sens. Environ. – volume: 191 start-page: 342 year: 2017 end-page: 358 ident: b0125 article-title: Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery publication-title: Remote Sens. Environ. – volume: 31 start-page: 127 year: 2014 end-page: 144 ident: b0070 article-title: Image inpainting: Overview and recent advances publication-title: IEEE Signal Process Mag. – volume: 96 start-page: 224 year: 2014 end-page: 235 ident: b0170 article-title: An effective thin cloud removal procedure for visible remote sensing images publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 56 start-page: 3047 year: 2018 end-page: 3061 ident: b0080 article-title: Nonlocal tensor completion for multitemporal remotely sensed images' inpainting publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 157 start-page: 93 year: 2019 end-page: 107 ident: b0030 article-title: Blind cloud and cloud shadow removal of multitemporal images based on total variation regularized low-rank sparsity decomposition publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 52 start-page: 7086 year: 2014 end-page: 7098 ident: b0105 article-title: Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 12 start-page: 882 year: 2003 end-page: 889 ident: b0010 article-title: Simultaneous structure and texture image inpainting publication-title: IEEE Trans. Image Process. – volume: 92 start-page: 54 year: 2014 end-page: 68 ident: b0040 article-title: Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 149 start-page: 215 year: 2019 end-page: 225 ident: b0220 article-title: Thin cloud removal from optical remote sensing images using the noise-adjusted principal components transform publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 33 start-page: 5644 year: 2012 end-page: 5676 ident: b0190 article-title: Remote-sensing image analysis and geostatistics publication-title: Int. J. Remote Sens. – volume: 10 start-page: 196 year: 2018 ident: b0250 article-title: Learning a dilated residual network for SAR image despeckling publication-title: Remote Sens. – volume: 13 start-page: 1200 year: 2004 end-page: 1212 ident: b0045 article-title: Region filling and object removal by exemplar-based image inpainting publication-title: IEEE Trans. Image Process. – volume: 140 start-page: 267 year: 2014 end-page: 278 ident: b0210 article-title: Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data publication-title: Remote Sens. Environ. – volume: 36 start-page: 2423 year: 2014 end-page: 2435 ident: b0075 article-title: Image completion approaches using the statistics of similar patches publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 1 year: 2019 end-page: 12 ident: b0260 article-title: A coarse-to-fine framework for cloud removal in remote sensing image sequence publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 216 start-page: 345 year: 2018 end-page: 354 ident: b0060 article-title: Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery publication-title: Remote Sens. Environ. – volume: 47 start-page: 613 year: 2009 end-page: 627 ident: b0155 article-title: Restoration of Aqua MODIS band 6 using histogram matching and local least squares fitting publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 49 start-page: 32 year: 1994 end-page: 40 ident: b0160 article-title: Kriging in the shadows: geostatistical interpolation for remote sensing publication-title: Remote Sens. Environ. – volume: 159 start-page: 269 year: 2015 end-page: 277 ident: b0280 article-title: Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images publication-title: Remote Sens. Environ. – reference: Di Mauro, N., Vergari, A., Basile, T.M.A., Ventola, F.G., Esposito, F., 2017. End-to-end learning of deep spatio-temporal representations for satellite image time series classification. In DC@ PKDD/ECML. – reference: Kingma, D.P., Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. – volume: 11 start-page: 1925 year: 2019 ident: b0120 article-title: Thick cloud removal in high-resolution satellite images using stepwise radiometric adjustment and residual correction publication-title: Remote Sens. – volume: 14 start-page: 1795 year: 2017 end-page: 1799 ident: b0205 article-title: Boosting the accuracy of multispectral image pansharpening by learning a deep residual network publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 521 start-page: 436 year: 2015 ident: b0095 article-title: Deep learning publication-title: Nature – volume: 57 start-page: 1 year: 2019 end-page: 13 ident: b0165 article-title: Spatial-spectral fusion by combining deep learning and variation model publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 148 start-page: 103 year: 2019 end-page: 113 ident: b0110 article-title: Cloud removal in remote sensing images using nonnegative matrix factorization and error correction publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 55 start-page: 3367 year: 2017 end-page: 3381 ident: b0135 article-title: An adaptive weighted tensor completion method for the recovery of remote sensing images with missing data publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 210 start-page: 259 year: 2018 end-page: 268 ident: b0180 article-title: Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis publication-title: Remote Sens. Environ. – start-page: 2754 year: 2016 ident: 10.1016/j.isprsjprs.2020.02.008_b0195 article-title: Removing clouds and recovering ground observations in satellite image sequences via temporally contiguous robust matrix completion publication-title: CVPR – volume: 92 start-page: 54 year: 2014 ident: 10.1016/j.isprsjprs.2020.02.008_b0040 article-title: Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2014.02.015 – volume: 131 start-page: 182 year: 2013 ident: 10.1016/j.isprsjprs.2020.02.008_b0235 article-title: Recovering missing pixels for Landsat ETM+ SLC-off imagery using multi-temporal regression analysis and a regularization method publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2012.12.012 – volume: 106 start-page: 1 year: 2015 ident: 10.1016/j.isprsjprs.2020.02.008_b0100 article-title: Sparse-based reconstruction of missing information in remote sensing images from spectral/temporal complementary information publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2015.03.009 – volume: 56 start-page: 4274 issue: 8 year: 2018 ident: 10.1016/j.isprsjprs.2020.02.008_b0255 article-title: Missing data reconstruction in remote sensing image with a unified spatial-temporal-spectral deep convolutional neural network publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2810208 – volume: 31 start-page: 127 issue: 1 year: 2014 ident: 10.1016/j.isprsjprs.2020.02.008_b0070 article-title: Image inpainting: Overview and recent advances publication-title: IEEE Signal Process Mag. doi: 10.1109/MSP.2013.2273004 – volume: 157 start-page: 93 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0030 article-title: Blind cloud and cloud shadow removal of multitemporal images based on total variation regularized low-rank sparsity decomposition publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2019.09.003 – volume: 149 start-page: 215 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0220 article-title: Thin cloud removal from optical remote sensing images using the noise-adjusted principal components transform publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2019.01.025 – volume: 10 start-page: 196 issue: 2 year: 2018 ident: 10.1016/j.isprsjprs.2020.02.008_b0250 article-title: Learning a dilated residual network for SAR image despeckling publication-title: Remote Sens. doi: 10.3390/rs10020196 – volume: 57 start-page: 1205 issue: 2 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0230 article-title: Hyperspectral Image denoising employing a spatial-spectral deep residual convolutional neural network publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2865197 – volume: 9 start-page: 521 issue: 3 year: 2012 ident: 10.1016/j.isprsjprs.2020.02.008_b0270 article-title: A modified neighborhood similar pixel interpolator approach for removing thick clouds in Landsat images publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2011.2173290 – ident: 10.1016/j.isprsjprs.2020.02.008_b0090 – volume: 140 start-page: 267 year: 2014 ident: 10.1016/j.isprsjprs.2020.02.008_b0210 article-title: Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2013.09.002 – volume: 52 start-page: 7086 issue: 11 year: 2014 ident: 10.1016/j.isprsjprs.2020.02.008_b0105 article-title: Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2014.2307354 – volume: 174 start-page: 109 year: 2016 ident: 10.1016/j.isprsjprs.2020.02.008_b0150 article-title: Spatiotemporal patterns of remotely sensed PM2. 5 concentration in China from 1999 to 2011 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2015.12.008 – volume: 199 start-page: 107 year: 2017 ident: 10.1016/j.isprsjprs.2020.02.008_b0185 article-title: Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4–8 images publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.07.002 – volume: 118 start-page: 83 year: 2012 ident: 10.1016/j.isprsjprs.2020.02.008_b0275 article-title: Object-based cloud and cloud shadow detection in Landsat imagery publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2011.10.028 – volume: 62 start-page: 1019 issue: 3 year: 2001 ident: 10.1016/j.isprsjprs.2020.02.008_b0015 article-title: Local inpainting models and TV inpainting publication-title: SIAM J. Appl. Math. – volume: 33 start-page: 5644 issue: 18 year: 2012 ident: 10.1016/j.isprsjprs.2020.02.008_b0190 article-title: Remote-sensing image analysis and geostatistics publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2012.666363 – volume: 36 start-page: 2423 issue: 12 year: 2014 ident: 10.1016/j.isprsjprs.2020.02.008_b0075 article-title: Image completion approaches using the statistics of similar patches publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2014.2330611 – volume: 221 start-page: 430 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0265 article-title: Deep learning based multi-temporal crop classification publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.11.032 – volume: 12 start-page: 882 issue: 8 year: 2003 ident: 10.1016/j.isprsjprs.2020.02.008_b0010 article-title: Simultaneous structure and texture image inpainting publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2003.815261 – volume: 3 start-page: 61 issue: 3 year: 2015 ident: 10.1016/j.isprsjprs.2020.02.008_b0175 article-title: Missing information reconstruction of remote sensing data: A technical review publication-title: IEEE Geosci. Remote Sens. Mag. doi: 10.1109/MGRS.2015.2441912 – volume: 96 start-page: 224 year: 2014 ident: 10.1016/j.isprsjprs.2020.02.008_b0170 article-title: An effective thin cloud removal procedure for visible remote sensing images publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2014.06.011 – volume: 55 start-page: 3656 issue: 7 year: 2017 ident: 10.1016/j.isprsjprs.2020.02.008_b0065 article-title: Multitemporal Landsat missing data recovery based on tempo-spectral angle model publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2017.2656162 – start-page: 1 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0260 article-title: A coarse-to-fine framework for cloud removal in remote sensing image sequence publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 11 start-page: 523 issue: 5 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0145 article-title: Temporal convolutional neural network for the classification of satellite image time series publication-title: Remote Sens. doi: 10.3390/rs11050523 – volume: 14 start-page: 1795 issue: 10 year: 2017 ident: 10.1016/j.isprsjprs.2020.02.008_b0205 article-title: Boosting the accuracy of multispectral image pansharpening by learning a deep residual network publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2017.2736020 – volume: 148 start-page: 103 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0110 article-title: Cloud removal in remote sensing images using nonnegative matrix factorization and error correction publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2018.12.013 – volume: 11 start-page: 978 issue: 3 year: 2018 ident: 10.1016/j.isprsjprs.2020.02.008_b0225 article-title: A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2018.2794888 – volume: 38 start-page: 295 issue: 2 year: 2016 ident: 10.1016/j.isprsjprs.2020.02.008_b0055 article-title: Image super-resolution using deep convolutional networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2015.2439281 – volume: 179 start-page: 183 year: 2016 ident: 10.1016/j.isprsjprs.2020.02.008_b0130 article-title: An empirical and radiative transfer model based algorithm to remove thin clouds in visible bands publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2016.03.034 – volume: 151 start-page: 176 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0025 article-title: Automatic mapping of planting year for tree crops with Landsat satellite time series stacks publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2019.03.012 – volume: 57 start-page: 1 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0165 article-title: Spatial-spectral fusion by combining deep learning and variation model publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2958096 – volume: 191 start-page: 342 year: 2017 ident: 10.1016/j.isprsjprs.2020.02.008_b0125 article-title: Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.01.026 – volume: 55 start-page: 3367 issue: 6 year: 2017 ident: 10.1016/j.isprsjprs.2020.02.008_b0135 article-title: An adaptive weighted tensor completion method for the recovery of remote sensing images with missing data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2017.2670021 – volume: 47 start-page: 613 issue: 2 year: 2009 ident: 10.1016/j.isprsjprs.2020.02.008_b0155 article-title: Restoration of Aqua MODIS band 6 using histogram matching and local least squares fitting publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2008.2003436 – volume: 49 start-page: 32 issue: 1 year: 1994 ident: 10.1016/j.isprsjprs.2020.02.008_b0160 article-title: Kriging in the shadows: geostatistical interpolation for remote sensing publication-title: Remote Sens. Environ. doi: 10.1016/0034-4257(94)90057-4 – volume: 11 start-page: 433 issue: 4 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0005 article-title: Validation of copernicus sentinel-2 cloud masks obtained from MAJA, Sen2Cor, and Fmask processors using reference cloud masks generated with a supervised active learning procedure publication-title: Remote Sens. doi: 10.3390/rs11040433 – volume: 210 start-page: 259 year: 2018 ident: 10.1016/j.isprsjprs.2020.02.008_b0180 article-title: Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.03.023 – volume: 157 start-page: 1 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0200 article-title: Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2019.08.017 – ident: 10.1016/j.isprsjprs.2020.02.008_b0050 – volume: 54 start-page: 2998 issue: 5 year: 2016 ident: 10.1016/j.isprsjprs.2020.02.008_b0215 article-title: Cloud removal based on sparse representation via multitemporal dictionary learning publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2015.2509860 – volume: 55 start-page: 27 issue: 1 year: 2017 ident: 10.1016/j.isprsjprs.2020.02.008_b0020 article-title: Spatially and temporally weighted regression: a novel method to produce continuous cloud-free Landsat imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2016.2580576 – volume: 115 start-page: 1053 issue: 4 year: 2011 ident: 10.1016/j.isprsjprs.2020.02.008_b0035 article-title: A simple and effective method for filling gaps in Landsat ETM+ SLC-off images publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2010.12.010 – volume: 13 start-page: 1200 issue: 9 year: 2004 ident: 10.1016/j.isprsjprs.2020.02.008_b0045 article-title: Region filling and object removal by exemplar-based image inpainting publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2004.833105 – volume: 216 start-page: 345 year: 2018 ident: 10.1016/j.isprsjprs.2020.02.008_b0060 article-title: Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.07.006 – volume: 57 start-page: 7317 issue: 10 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0245 article-title: Hybrid noise removal in hyperspectral imagery with a spatial-spectral gradient network publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2912909 – volume: 56 start-page: 3047 issue: 6 year: 2018 ident: 10.1016/j.isprsjprs.2020.02.008_b0080 article-title: Nonlocal tensor completion for multitemporal remotely sensed images' inpainting publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2790262 – volume: 11 start-page: 1925 issue: 16 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0120 article-title: Thick cloud removal in high-resolution satellite images using stepwise radiometric adjustment and residual correction publication-title: Remote Sens. doi: 10.3390/rs11161925 – volume: 150 start-page: 197 year: 2019 ident: 10.1016/j.isprsjprs.2020.02.008_b0115 article-title: Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2019.02.017 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 10.1016/j.isprsjprs.2020.02.008_b0095 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – start-page: 675 year: 2014 ident: 10.1016/j.isprsjprs.2020.02.008_b0085 article-title: Caffe: Convolutional architecture for fast feature embedding – volume: 26 start-page: 3142 issue: 7 year: 2017 ident: 10.1016/j.isprsjprs.2020.02.008_b0240 article-title: Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2662206 – volume: 12 start-page: 27 year: 2010 ident: 10.1016/j.isprsjprs.2020.02.008_b0140 article-title: Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms publication-title: Int. J. Appl. Earth Obs. Geoinf. doi: 10.1016/j.jag.2009.11.002 – volume: 159 start-page: 269 year: 2015 ident: 10.1016/j.isprsjprs.2020.02.008_b0280 article-title: Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2014.12.014 |
| SSID | ssj0001568 |
| Score | 2.6058853 |
| Snippet | Thick cloud and its shadow severely reduce the data usability of optical satellite remote sensing data. Although many approaches have been presented for cloud... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 148 |
| SubjectTerms | cloud cover Gap-filling geospatial data processing Global-local CNN image analysis Landsat Patch group Progressive iteration remote sensing spatial data Spatio-temporal Thick cloud and cloud shadow |
| Title | Thick cloud and cloud shadow removal in multitemporal imagery using progressively spatio-temporal patch group deep learning |
| URI | https://dx.doi.org/10.1016/j.isprsjprs.2020.02.008 https://www.proquest.com/docview/2400525685 |
| Volume | 162 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEA6iB_UgPnF9EcFr3Ww3zbbeZHFZFb2o4C2kydStru2yD2UR_O1O2nR9gHiQ0tKUCS2Z6Tzab2YIOUqU0n7LTzyApvJ4CA0vjmw2mh-KEBTauNgmOF9di-4dv7gP7udIu8qFsbBKp_tLnV5oa3el7lazPkjT-g3D0MG3BZBYs0B32Ax23rJSfvz-CfNolOlwltiz1N8wXuloMBw94o6Bos_K4p3hbxbqh64uDFBnlaw4z5Gelg-3RuYgWyfLX-oJrpNF19K8N90gb7e9VD9R3c8nhqrMuLNRT5n8lQ7hOUcZo2lGC0ihq1CFF55tUYsptXj4B1qgtyxQ9gX6Uzoq0NfejBaHukeLvBBqAAbUtaB42CR3nbPbdtdznRY8zRt87HEd6CBJjMBgw8QmZMCNMKFRLEoChV6R0eiZacEiiIMwjBgEpoWeHoDQRjDe3CLzWZ7BNqGJaGgT-EnMDXAMH3FLIsBj3Ip9rlSNiGp1pXZlyG03jL6s8GaPcsYWadkimS-RLTXCZhMHZSWOv6ecVOyT34RKor34e_JhxXCJr5z9j6IyyCdIxO3HdJSrYOc_N9glS3ZUwoD2yPx4OIF99HDG8UEhwgdk4fT8snv9ATe1AU0 |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dT9swED-x8sB4QIxtGjA2T9prVjd13GRvUwXqxsfLisSb5dgXGlaSqh-gCom_fefEKTBp4gFFiRLnrEQ--z6S390BfM20NmEvzALErg5EjJ0gTVw0WhjLGDXpuNQFOJ-eycG5-HURXaxBv4mFcbBKL_trmV5Ja9_S9qPZnuR5-zcn1yF0CZB4t0J3vIJ1QcvXlTH4dv-A8-jU8XCOOnDkT0Be-WwynV3RTp5iyOvsnfH_VNQ_wrrSQEfbsOVNR_ajfrs3sIbFDmw-Sii4Axu-pvlo-RbuhqPc_GFmXC4s04X1Z7ORtuUtm-J1SZOM5QWrMIU-RRU1XLusFkvmAPGXrIJvOaTsDY6XbFbBr4MVLV2aEasCQ5hFnDBfg-LyHZwfHQ77g8CXWgiM6Ih5IExkoiyzkrwNm9qYo7DSxlbzJIs0mUXWkGlmJE8wjeI44RjZHpl6iNJYyUX3PbSKssAPwDLZMTYKs1RYFOQ_0pYlSMe0l4ZC612Qzegq4_OQu3IYY9UAzq7Uii3KsUXxUBFbdoGvOk7qVBzPd_nesE89mVWKFMbznb80DFe05tyPFF1guSAi4b6m07yK9l7ygM-wMRienqiTn2fH-_Da3akxQR-hNZ8u8IDMnXn6qZrOfwEE0ALb |
| 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=Thick+cloud+and+cloud+shadow+removal+in+multitemporal+imagery+using+progressively+spatio-temporal+patch+group+deep+learning&rft.jtitle=ISPRS+journal+of+photogrammetry+and+remote+sensing&rft.au=Zhang%2C+Qiang&rft.au=Yuan%2C+Qiangqiang&rft.au=Li%2C+Jie&rft.au=Li%2C+Zhiwei&rft.date=2020-04-01&rft.issn=0924-2716&rft.volume=162+p.148-160&rft.spage=148&rft.epage=160&rft_id=info:doi/10.1016%2Fj.isprsjprs.2020.02.008&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0924-2716&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0924-2716&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0924-2716&client=summon |