Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images

A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies o...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ming-Der Yang [verfasserIn] Hsin-Hung Tseng [verfasserIn] Yu-Chun Hsu [verfasserIn] Hui Ping Tsai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	semantic segmentation deep learning lodging uav vegetation index

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 12(2020), 4, p 633
Übergeordnetes Werk:	volume:12 ; year:2020 ; number:4, p 633

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs12040633

Katalog-ID:	DOAJ013661957

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ013661957
003	DE-627
005	20230310060022.0
007	cr uuu---uuuuu
008	230226s2020 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/rs12040633 \|2 doi
035			\|a (DE-627)DOAJ013661957
035			\|a (DE-599)DOAJ6f5678ffb1124caa8bb0ee25f301923f
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	0		\|a Ming-Der Yang \|e verfasserin \|4 aut
245	1	0	\|a Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images
264		1	\|c 2020
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method.
650		4	\|a semantic segmentation
650		4	\|a deep learning
650		4	\|a lodging
650		4	\|a uav
650		4	\|a vegetation index
653		0	\|a Science
653		0	\|a Q
700	0		\|a Hsin-Hung Tseng \|e verfasserin \|4 aut
700	0		\|a Yu-Chun Hsu \|e verfasserin \|4 aut
700	0		\|a Hui Ping Tsai \|e verfasserin \|4 aut
773	0	8	\|i In \|t Remote Sensing \|d MDPI AG, 2009 \|g 12(2020), 4, p 633 \|w (DE-627)608937916 \|w (DE-600)2513863-7 \|x 20724292 \|7 nnns
773	1	8	\|g volume:12 \|g year:2020 \|g number:4, p 633
856	4	0	\|u https://doi.org/10.3390/rs12040633 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/6f5678ffb1124caa8bb0ee25f301923f \|z kostenfrei
856	4	0	\|u https://www.mdpi.com/2072-4292/12/4/633 \|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 12 \|j 2020 \|e 4, p 633

Indexfelder

author_variant	m d y mdy h h t hht y c h ych h p t hpt
matchkey_str	article:20724292:2020----::eatcemnainsndelannwtvgttoidcsorcldigdniiai
hierarchy_sort_str	2020
publishDate	2020
allfields	10.3390/rs12040633 doi (DE-627)DOAJ013661957 (DE-599)DOAJ6f5678ffb1124caa8bb0ee25f301923f DE-627 ger DE-627 rakwb eng Ming-Der Yang verfasserin aut Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method. semantic segmentation deep learning lodging uav vegetation index Science Q Hsin-Hung Tseng verfasserin aut Yu-Chun Hsu verfasserin aut Hui Ping Tsai verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 4, p 633 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:4, p 633 https://doi.org/10.3390/rs12040633 kostenfrei https://doaj.org/article/6f5678ffb1124caa8bb0ee25f301923f kostenfrei https://www.mdpi.com/2072-4292/12/4/633 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 12 2020 4, p 633
spelling	10.3390/rs12040633 doi (DE-627)DOAJ013661957 (DE-599)DOAJ6f5678ffb1124caa8bb0ee25f301923f DE-627 ger DE-627 rakwb eng Ming-Der Yang verfasserin aut Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method. semantic segmentation deep learning lodging uav vegetation index Science Q Hsin-Hung Tseng verfasserin aut Yu-Chun Hsu verfasserin aut Hui Ping Tsai verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 4, p 633 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:4, p 633 https://doi.org/10.3390/rs12040633 kostenfrei https://doaj.org/article/6f5678ffb1124caa8bb0ee25f301923f kostenfrei https://www.mdpi.com/2072-4292/12/4/633 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 12 2020 4, p 633
allfields_unstemmed	10.3390/rs12040633 doi (DE-627)DOAJ013661957 (DE-599)DOAJ6f5678ffb1124caa8bb0ee25f301923f DE-627 ger DE-627 rakwb eng Ming-Der Yang verfasserin aut Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method. semantic segmentation deep learning lodging uav vegetation index Science Q Hsin-Hung Tseng verfasserin aut Yu-Chun Hsu verfasserin aut Hui Ping Tsai verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 4, p 633 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:4, p 633 https://doi.org/10.3390/rs12040633 kostenfrei https://doaj.org/article/6f5678ffb1124caa8bb0ee25f301923f kostenfrei https://www.mdpi.com/2072-4292/12/4/633 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 12 2020 4, p 633
allfieldsGer	10.3390/rs12040633 doi (DE-627)DOAJ013661957 (DE-599)DOAJ6f5678ffb1124caa8bb0ee25f301923f DE-627 ger DE-627 rakwb eng Ming-Der Yang verfasserin aut Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method. semantic segmentation deep learning lodging uav vegetation index Science Q Hsin-Hung Tseng verfasserin aut Yu-Chun Hsu verfasserin aut Hui Ping Tsai verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 4, p 633 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:4, p 633 https://doi.org/10.3390/rs12040633 kostenfrei https://doaj.org/article/6f5678ffb1124caa8bb0ee25f301923f kostenfrei https://www.mdpi.com/2072-4292/12/4/633 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 12 2020 4, p 633
allfieldsSound	10.3390/rs12040633 doi (DE-627)DOAJ013661957 (DE-599)DOAJ6f5678ffb1124caa8bb0ee25f301923f DE-627 ger DE-627 rakwb eng Ming-Der Yang verfasserin aut Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method. semantic segmentation deep learning lodging uav vegetation index Science Q Hsin-Hung Tseng verfasserin aut Yu-Chun Hsu verfasserin aut Hui Ping Tsai verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 4, p 633 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:4, p 633 https://doi.org/10.3390/rs12040633 kostenfrei https://doaj.org/article/6f5678ffb1124caa8bb0ee25f301923f kostenfrei https://www.mdpi.com/2072-4292/12/4/633 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 12 2020 4, p 633
language	English
source	In Remote Sensing 12(2020), 4, p 633 volume:12 year:2020 number:4, p 633
sourceStr	In Remote Sensing 12(2020), 4, p 633 volume:12 year:2020 number:4, p 633
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	semantic segmentation deep learning lodging uav vegetation index Science Q
isfreeaccess_bool	true
container_title	Remote Sensing
authorswithroles_txt_mv	Ming-Der Yang @@aut@@ Hsin-Hung Tseng @@aut@@ Yu-Chun Hsu @@aut@@ Hui Ping Tsai @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	608937916
id	DOAJ013661957
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">DOAJ013661957</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310060022.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/rs12040633</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ013661957</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ6f5678ffb1124caa8bb0ee25f301923f</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">Ming-Der Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</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">A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">semantic segmentation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">lodging</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">uav</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">vegetation index</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">Hsin-Hung Tseng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yu-Chun Hsu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hui Ping Tsai</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">12(2020), 4, p 633</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:12</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:4, p 633</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/rs12040633</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/6f5678ffb1124caa8bb0ee25f301923f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2072-4292/12/4/633</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">12</subfield><subfield code="j">2020</subfield><subfield code="e">4, p 633</subfield></datafield></record></collection>
author	Ming-Der Yang
spellingShingle	Ming-Der Yang misc semantic segmentation misc deep learning misc lodging misc uav misc vegetation index misc Science misc Q Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images
authorStr	Ming-Der Yang
ppnlink_with_tag_str_mv	@@773@@(DE-627)608937916
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut
collection	DOAJ
remote_str	true
illustrated	Not Illustrated
issn	20724292
topic_title	Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images semantic segmentation deep learning lodging uav vegetation index
topic	misc semantic segmentation misc deep learning misc lodging misc uav misc vegetation index misc Science misc Q
topic_unstemmed	misc semantic segmentation misc deep learning misc lodging misc uav misc vegetation index misc Science misc Q
topic_browse	misc semantic segmentation misc deep learning misc lodging misc uav misc vegetation index 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	Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images
ctrlnum	(DE-627)DOAJ013661957 (DE-599)DOAJ6f5678ffb1124caa8bb0ee25f301923f
title_full	Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images
author_sort	Ming-Der Yang
journal	Remote Sensing
journalStr	Remote Sensing
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2020
contenttype_str_mv	txt
author_browse	Ming-Der Yang Hsin-Hung Tseng Yu-Chun Hsu Hui Ping Tsai
container_volume	12
format_se	Elektronische Aufsätze
author-letter	Ming-Der Yang
doi_str_mv	10.3390/rs12040633
author2-role	verfasserin
title_sort	semantic segmentation using deep learning with vegetation indices for rice lodging identification in multi-date uav visible images
title_auth	Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images
abstract	A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method.
abstractGer	A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method.
abstract_unstemmed	A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method.
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	4, p 633
title_short	Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images
url	https://doi.org/10.3390/rs12040633 https://doaj.org/article/6f5678ffb1124caa8bb0ee25f301923f https://www.mdpi.com/2072-4292/12/4/633 https://doaj.org/toc/2072-4292
remote_bool	true
author2	Hsin-Hung Tseng Yu-Chun Hsu Hui Ping Tsai
author2Str	Hsin-Hung Tseng Yu-Chun Hsu Hui Ping Tsai
ppnlink	608937916
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/rs12040633
up_date	2024-07-03T18:52:28.755Z
_version_	1803585059559571456
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">DOAJ013661957</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310060022.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/rs12040633</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ013661957</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ6f5678ffb1124caa8bb0ee25f301923f</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">Ming-Der Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</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">A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">semantic segmentation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">lodging</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">uav</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">vegetation index</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">Hsin-Hung Tseng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yu-Chun Hsu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hui Ping Tsai</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">12(2020), 4, p 633</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:12</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:4, p 633</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/rs12040633</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/6f5678ffb1124caa8bb0ee25f301923f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2072-4292/12/4/633</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">12</subfield><subfield code="j">2020</subfield><subfield code="e">4, p 633</subfield></datafield></record></collection>
score	7.399703

Nicht das Richtige dabei?

Schreiben Sie uns!

Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?