Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV)
Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used...
Ausführliche Beschreibung
Autor*in: |
Yilin Wang [verfasserIn] Dongxu Yin [verfasserIn] Liming Lou [verfasserIn] Xinying Li [verfasserIn] Pengle Cheng [verfasserIn] Ying Huang [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2022 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: Remote Sensing - MDPI AG, 2009, 14(2022), 16, p 4043 |
---|---|
Übergeordnetes Werk: |
volume:14 ; year:2022 ; number:16, p 4043 |
Links: |
---|
DOI / URN: |
10.3390/rs14164043 |
---|
Katalog-ID: |
DOAJ036309826 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ036309826 | ||
003 | DE-627 | ||
005 | 20240414072809.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230227s2022 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3390/rs14164043 |2 doi | |
035 | |a (DE-627)DOAJ036309826 | ||
035 | |a (DE-599)DOAJ4ada259f4b1340459f120ad52a5c5c77 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 0 | |a Yilin Wang |e verfasserin |4 aut | |
245 | 1 | 0 | |a Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV) |
264 | 1 | |c 2022 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation. | ||
650 | 4 | |a UAV | |
650 | 4 | |a deep learning | |
650 | 4 | |a remote sensing interpretation | |
653 | 0 | |a Science | |
653 | 0 | |a Q | |
700 | 0 | |a Dongxu Yin |e verfasserin |4 aut | |
700 | 0 | |a Liming Lou |e verfasserin |4 aut | |
700 | 0 | |a Xinying Li |e verfasserin |4 aut | |
700 | 0 | |a Pengle Cheng |e verfasserin |4 aut | |
700 | 0 | |a Ying Huang |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Remote Sensing |d MDPI AG, 2009 |g 14(2022), 16, p 4043 |w (DE-627)608937916 |w (DE-600)2513863-7 |x 20724292 |7 nnns |
773 | 1 | 8 | |g volume:14 |g year:2022 |g number:16, p 4043 |
856 | 4 | 0 | |u https://doi.org/10.3390/rs14164043 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/4ada259f4b1340459f120ad52a5c5c77 |z kostenfrei |
856 | 4 | 0 | |u https://www.mdpi.com/2072-4292/14/16/4043 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2072-4292 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2108 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2119 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4392 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 14 |j 2022 |e 16, p 4043 |
author_variant |
y w yw d y dy l l ll x l xl p c pc y h yh |
---|---|
matchkey_str |
article:20724292:2022----::utoonanatdmcvrnepeainobnndelannaddiaeodt |
hierarchy_sort_str |
2022 |
publishDate |
2022 |
allfields |
10.3390/rs14164043 doi (DE-627)DOAJ036309826 (DE-599)DOAJ4ada259f4b1340459f120ad52a5c5c77 DE-627 ger DE-627 rakwb eng Yilin Wang verfasserin aut Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV) 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation. UAV deep learning remote sensing interpretation Science Q Dongxu Yin verfasserin aut Liming Lou verfasserin aut Xinying Li verfasserin aut Pengle Cheng verfasserin aut Ying Huang verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 16, p 4043 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:16, p 4043 https://doi.org/10.3390/rs14164043 kostenfrei https://doaj.org/article/4ada259f4b1340459f120ad52a5c5c77 kostenfrei https://www.mdpi.com/2072-4292/14/16/4043 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 16, p 4043 |
spelling |
10.3390/rs14164043 doi (DE-627)DOAJ036309826 (DE-599)DOAJ4ada259f4b1340459f120ad52a5c5c77 DE-627 ger DE-627 rakwb eng Yilin Wang verfasserin aut Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV) 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation. UAV deep learning remote sensing interpretation Science Q Dongxu Yin verfasserin aut Liming Lou verfasserin aut Xinying Li verfasserin aut Pengle Cheng verfasserin aut Ying Huang verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 16, p 4043 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:16, p 4043 https://doi.org/10.3390/rs14164043 kostenfrei https://doaj.org/article/4ada259f4b1340459f120ad52a5c5c77 kostenfrei https://www.mdpi.com/2072-4292/14/16/4043 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 16, p 4043 |
allfields_unstemmed |
10.3390/rs14164043 doi (DE-627)DOAJ036309826 (DE-599)DOAJ4ada259f4b1340459f120ad52a5c5c77 DE-627 ger DE-627 rakwb eng Yilin Wang verfasserin aut Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV) 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation. UAV deep learning remote sensing interpretation Science Q Dongxu Yin verfasserin aut Liming Lou verfasserin aut Xinying Li verfasserin aut Pengle Cheng verfasserin aut Ying Huang verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 16, p 4043 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:16, p 4043 https://doi.org/10.3390/rs14164043 kostenfrei https://doaj.org/article/4ada259f4b1340459f120ad52a5c5c77 kostenfrei https://www.mdpi.com/2072-4292/14/16/4043 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 16, p 4043 |
allfieldsGer |
10.3390/rs14164043 doi (DE-627)DOAJ036309826 (DE-599)DOAJ4ada259f4b1340459f120ad52a5c5c77 DE-627 ger DE-627 rakwb eng Yilin Wang verfasserin aut Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV) 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation. UAV deep learning remote sensing interpretation Science Q Dongxu Yin verfasserin aut Liming Lou verfasserin aut Xinying Li verfasserin aut Pengle Cheng verfasserin aut Ying Huang verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 16, p 4043 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:16, p 4043 https://doi.org/10.3390/rs14164043 kostenfrei https://doaj.org/article/4ada259f4b1340459f120ad52a5c5c77 kostenfrei https://www.mdpi.com/2072-4292/14/16/4043 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 16, p 4043 |
allfieldsSound |
10.3390/rs14164043 doi (DE-627)DOAJ036309826 (DE-599)DOAJ4ada259f4b1340459f120ad52a5c5c77 DE-627 ger DE-627 rakwb eng Yilin Wang verfasserin aut Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV) 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation. UAV deep learning remote sensing interpretation Science Q Dongxu Yin verfasserin aut Liming Lou verfasserin aut Xinying Li verfasserin aut Pengle Cheng verfasserin aut Ying Huang verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 16, p 4043 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:16, p 4043 https://doi.org/10.3390/rs14164043 kostenfrei https://doaj.org/article/4ada259f4b1340459f120ad52a5c5c77 kostenfrei https://www.mdpi.com/2072-4292/14/16/4043 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 16, p 4043 |
language |
English |
source |
In Remote Sensing 14(2022), 16, p 4043 volume:14 year:2022 number:16, p 4043 |
sourceStr |
In Remote Sensing 14(2022), 16, p 4043 volume:14 year:2022 number:16, p 4043 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
UAV deep learning remote sensing interpretation Science Q |
isfreeaccess_bool |
true |
container_title |
Remote Sensing |
authorswithroles_txt_mv |
Yilin Wang @@aut@@ Dongxu Yin @@aut@@ Liming Lou @@aut@@ Xinying Li @@aut@@ Pengle Cheng @@aut@@ Ying Huang @@aut@@ |
publishDateDaySort_date |
2022-01-01T00:00:00Z |
hierarchy_top_id |
608937916 |
id |
DOAJ036309826 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ036309826</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414072809.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/rs14164043</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ036309826</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ4ada259f4b1340459f120ad52a5c5c77</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Yilin Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV)</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">UAV</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">remote sensing interpretation</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Science</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Q</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Dongxu Yin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Liming Lou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xinying Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Pengle Cheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ying Huang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Remote Sensing</subfield><subfield code="d">MDPI AG, 2009</subfield><subfield code="g">14(2022), 16, p 4043</subfield><subfield code="w">(DE-627)608937916</subfield><subfield code="w">(DE-600)2513863-7</subfield><subfield code="x">20724292</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:16, p 4043</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/rs14164043</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/4ada259f4b1340459f120ad52a5c5c77</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2072-4292/14/16/4043</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2072-4292</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4392</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2022</subfield><subfield code="e">16, p 4043</subfield></datafield></record></collection>
|
author |
Yilin Wang |
spellingShingle |
Yilin Wang misc UAV misc deep learning misc remote sensing interpretation misc Science misc Q Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV) |
authorStr |
Yilin Wang |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)608937916 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
illustrated |
Not Illustrated |
issn |
20724292 |
topic_title |
Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV) UAV deep learning remote sensing interpretation |
topic |
misc UAV misc deep learning misc remote sensing interpretation misc Science misc Q |
topic_unstemmed |
misc UAV misc deep learning misc remote sensing interpretation misc Science misc Q |
topic_browse |
misc UAV misc deep learning misc remote sensing interpretation misc Science misc Q |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Remote Sensing |
hierarchy_parent_id |
608937916 |
hierarchy_top_title |
Remote Sensing |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)608937916 (DE-600)2513863-7 |
title |
Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV) |
ctrlnum |
(DE-627)DOAJ036309826 (DE-599)DOAJ4ada259f4b1340459f120ad52a5c5c77 |
title_full |
Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV) |
author_sort |
Yilin Wang |
journal |
Remote Sensing |
journalStr |
Remote Sensing |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2022 |
contenttype_str_mv |
txt |
author_browse |
Yilin Wang Dongxu Yin Liming Lou Xinying Li Pengle Cheng Ying Huang |
container_volume |
14 |
format_se |
Elektronische Aufsätze |
author-letter |
Yilin Wang |
doi_str_mv |
10.3390/rs14164043 |
author2-role |
verfasserin |
title_sort |
luotuo mountain waste dump cover interpretation combining deep learning and vdvi based on data from an unmanned aerial vehicle (uav) |
title_auth |
Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV) |
abstract |
Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation. |
abstractGer |
Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation. |
abstract_unstemmed |
Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 |
container_issue |
16, p 4043 |
title_short |
Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV) |
url |
https://doi.org/10.3390/rs14164043 https://doaj.org/article/4ada259f4b1340459f120ad52a5c5c77 https://www.mdpi.com/2072-4292/14/16/4043 https://doaj.org/toc/2072-4292 |
remote_bool |
true |
author2 |
Dongxu Yin Liming Lou Xinying Li Pengle Cheng Ying Huang |
author2Str |
Dongxu Yin Liming Lou Xinying Li Pengle Cheng Ying Huang |
ppnlink |
608937916 |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.3390/rs14164043 |
up_date |
2024-07-03T19:51:46.210Z |
_version_ |
1803588789823602689 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ036309826</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414072809.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/rs14164043</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ036309826</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ4ada259f4b1340459f120ad52a5c5c77</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Yilin Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV)</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">UAV</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">remote sensing interpretation</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Science</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Q</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Dongxu Yin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Liming Lou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xinying Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Pengle Cheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ying Huang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Remote Sensing</subfield><subfield code="d">MDPI AG, 2009</subfield><subfield code="g">14(2022), 16, p 4043</subfield><subfield code="w">(DE-627)608937916</subfield><subfield code="w">(DE-600)2513863-7</subfield><subfield code="x">20724292</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:16, p 4043</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/rs14164043</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/4ada259f4b1340459f120ad52a5c5c77</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2072-4292/14/16/4043</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2072-4292</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4392</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2022</subfield><subfield code="e">16, p 4043</subfield></datafield></record></collection>
|
score |
7.399967 |