Temporal convolutional neural network for land use and land cover classification using satellite images time series
Abstract Satellite image time series provide essential information to closely monitor vegetation dynamics, revealing growth pattern and phenological characteristics of crops. In this study, we explored a one-dimension Temporal Convolutional Neural Network (1D-TempCNN) model for land use and land cov...
Ausführliche Beschreibung
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| Autor*in: |
Ló, Thiago Berticelli [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© Saudi Society for Geosciences and Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Arabian journal of geosciences - Berlin : Springer, 2008, 16(2023), 10 vom: 29. Sept. |
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| Übergeordnetes Werk: |
volume:16 ; year:2023 ; number:10 ; day:29 ; month:09 |
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| DOI / URN: |
10.1007/s12517-023-11688-4 |
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SPR053237412 |
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| 520 | |a Abstract Satellite image time series provide essential information to closely monitor vegetation dynamics, revealing growth pattern and phenological characteristics of crops. In this study, we explored a one-dimension Temporal Convolutional Neural Network (1D-TempCNN) model for land use and land cover classification from Sentinel-2 image time series. The main goal was to evaluate the generalization ability by comparing 1D-TempCNN with two classical methods, Support Vector Machine (SVM) and Random Forest (RF). The experiments were conducted in three different sites, each one with its own crop cycles and time series compositions, and we performed cross-site and cross-year tests. These cross-testing approaches allow examining the ability of the classification models to generalize temporal (and spectral) patterns. Two data augmentation techniques, sliding window and scaling, were used to increase the amount and diversity of data. Our results show that data augmentation technique was essential to handle the data variability, contributing to the generalization of models and overall performance, resulting in an increase of up to 19.72 percentage points in overall accuracy (OA). Additionally, results show that 1D-TempCNN achieved superior OA (94.34–98.67%) in cross-testing, outperforming SVM (88.32–97.45%) and RF (90.25–98.12%). This demonstrates that the proposed 1D-TempCNN model, combined with data augmentation techniques, is capable of higher generalization for land use and land cover classification. | ||
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10.1007/s12517-023-11688-4 doi (DE-627)SPR053237412 (SPR)s12517-023-11688-4-e DE-627 ger DE-627 rakwb eng Ló, Thiago Berticelli verfasserin (orcid)0000-0002-1457-8810 aut Temporal convolutional neural network for land use and land cover classification using satellite images time series 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences and Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Satellite image time series provide essential information to closely monitor vegetation dynamics, revealing growth pattern and phenological characteristics of crops. In this study, we explored a one-dimension Temporal Convolutional Neural Network (1D-TempCNN) model for land use and land cover classification from Sentinel-2 image time series. The main goal was to evaluate the generalization ability by comparing 1D-TempCNN with two classical methods, Support Vector Machine (SVM) and Random Forest (RF). The experiments were conducted in three different sites, each one with its own crop cycles and time series compositions, and we performed cross-site and cross-year tests. These cross-testing approaches allow examining the ability of the classification models to generalize temporal (and spectral) patterns. Two data augmentation techniques, sliding window and scaling, were used to increase the amount and diversity of data. Our results show that data augmentation technique was essential to handle the data variability, contributing to the generalization of models and overall performance, resulting in an increase of up to 19.72 percentage points in overall accuracy (OA). Additionally, results show that 1D-TempCNN achieved superior OA (94.34–98.67%) in cross-testing, outperforming SVM (88.32–97.45%) and RF (90.25–98.12%). This demonstrates that the proposed 1D-TempCNN model, combined with data augmentation techniques, is capable of higher generalization for land use and land cover classification. Data augmentation (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Sentinel-2 (dpeaa)DE-He213 Cross-site testing (dpeaa)DE-He213 Corrêa, Ulisses Brisolara (orcid)0000-0001-6695-3451 aut Araújo, Ricardo Matsumura (orcid)0000-0003-0514-8883 aut Johann, Jerry Adriani (orcid)0000-0001-6184-8011 aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 16(2023), 10 vom: 29. Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:16 year:2023 number:10 day:29 month:09 https://dx.doi.org/10.1007/s12517-023-11688-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2023 10 29 09 |
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10.1007/s12517-023-11688-4 doi (DE-627)SPR053237412 (SPR)s12517-023-11688-4-e DE-627 ger DE-627 rakwb eng Ló, Thiago Berticelli verfasserin (orcid)0000-0002-1457-8810 aut Temporal convolutional neural network for land use and land cover classification using satellite images time series 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences and Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Satellite image time series provide essential information to closely monitor vegetation dynamics, revealing growth pattern and phenological characteristics of crops. In this study, we explored a one-dimension Temporal Convolutional Neural Network (1D-TempCNN) model for land use and land cover classification from Sentinel-2 image time series. The main goal was to evaluate the generalization ability by comparing 1D-TempCNN with two classical methods, Support Vector Machine (SVM) and Random Forest (RF). The experiments were conducted in three different sites, each one with its own crop cycles and time series compositions, and we performed cross-site and cross-year tests. These cross-testing approaches allow examining the ability of the classification models to generalize temporal (and spectral) patterns. Two data augmentation techniques, sliding window and scaling, were used to increase the amount and diversity of data. Our results show that data augmentation technique was essential to handle the data variability, contributing to the generalization of models and overall performance, resulting in an increase of up to 19.72 percentage points in overall accuracy (OA). Additionally, results show that 1D-TempCNN achieved superior OA (94.34–98.67%) in cross-testing, outperforming SVM (88.32–97.45%) and RF (90.25–98.12%). This demonstrates that the proposed 1D-TempCNN model, combined with data augmentation techniques, is capable of higher generalization for land use and land cover classification. Data augmentation (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Sentinel-2 (dpeaa)DE-He213 Cross-site testing (dpeaa)DE-He213 Corrêa, Ulisses Brisolara (orcid)0000-0001-6695-3451 aut Araújo, Ricardo Matsumura (orcid)0000-0003-0514-8883 aut Johann, Jerry Adriani (orcid)0000-0001-6184-8011 aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 16(2023), 10 vom: 29. Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:16 year:2023 number:10 day:29 month:09 https://dx.doi.org/10.1007/s12517-023-11688-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2023 10 29 09 |
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10.1007/s12517-023-11688-4 doi (DE-627)SPR053237412 (SPR)s12517-023-11688-4-e DE-627 ger DE-627 rakwb eng Ló, Thiago Berticelli verfasserin (orcid)0000-0002-1457-8810 aut Temporal convolutional neural network for land use and land cover classification using satellite images time series 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences and Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Satellite image time series provide essential information to closely monitor vegetation dynamics, revealing growth pattern and phenological characteristics of crops. In this study, we explored a one-dimension Temporal Convolutional Neural Network (1D-TempCNN) model for land use and land cover classification from Sentinel-2 image time series. The main goal was to evaluate the generalization ability by comparing 1D-TempCNN with two classical methods, Support Vector Machine (SVM) and Random Forest (RF). The experiments were conducted in three different sites, each one with its own crop cycles and time series compositions, and we performed cross-site and cross-year tests. These cross-testing approaches allow examining the ability of the classification models to generalize temporal (and spectral) patterns. Two data augmentation techniques, sliding window and scaling, were used to increase the amount and diversity of data. Our results show that data augmentation technique was essential to handle the data variability, contributing to the generalization of models and overall performance, resulting in an increase of up to 19.72 percentage points in overall accuracy (OA). Additionally, results show that 1D-TempCNN achieved superior OA (94.34–98.67%) in cross-testing, outperforming SVM (88.32–97.45%) and RF (90.25–98.12%). This demonstrates that the proposed 1D-TempCNN model, combined with data augmentation techniques, is capable of higher generalization for land use and land cover classification. Data augmentation (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Sentinel-2 (dpeaa)DE-He213 Cross-site testing (dpeaa)DE-He213 Corrêa, Ulisses Brisolara (orcid)0000-0001-6695-3451 aut Araújo, Ricardo Matsumura (orcid)0000-0003-0514-8883 aut Johann, Jerry Adriani (orcid)0000-0001-6184-8011 aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 16(2023), 10 vom: 29. Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:16 year:2023 number:10 day:29 month:09 https://dx.doi.org/10.1007/s12517-023-11688-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2023 10 29 09 |
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10.1007/s12517-023-11688-4 doi (DE-627)SPR053237412 (SPR)s12517-023-11688-4-e DE-627 ger DE-627 rakwb eng Ló, Thiago Berticelli verfasserin (orcid)0000-0002-1457-8810 aut Temporal convolutional neural network for land use and land cover classification using satellite images time series 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences and Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Satellite image time series provide essential information to closely monitor vegetation dynamics, revealing growth pattern and phenological characteristics of crops. In this study, we explored a one-dimension Temporal Convolutional Neural Network (1D-TempCNN) model for land use and land cover classification from Sentinel-2 image time series. The main goal was to evaluate the generalization ability by comparing 1D-TempCNN with two classical methods, Support Vector Machine (SVM) and Random Forest (RF). The experiments were conducted in three different sites, each one with its own crop cycles and time series compositions, and we performed cross-site and cross-year tests. These cross-testing approaches allow examining the ability of the classification models to generalize temporal (and spectral) patterns. Two data augmentation techniques, sliding window and scaling, were used to increase the amount and diversity of data. Our results show that data augmentation technique was essential to handle the data variability, contributing to the generalization of models and overall performance, resulting in an increase of up to 19.72 percentage points in overall accuracy (OA). Additionally, results show that 1D-TempCNN achieved superior OA (94.34–98.67%) in cross-testing, outperforming SVM (88.32–97.45%) and RF (90.25–98.12%). This demonstrates that the proposed 1D-TempCNN model, combined with data augmentation techniques, is capable of higher generalization for land use and land cover classification. Data augmentation (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Sentinel-2 (dpeaa)DE-He213 Cross-site testing (dpeaa)DE-He213 Corrêa, Ulisses Brisolara (orcid)0000-0001-6695-3451 aut Araújo, Ricardo Matsumura (orcid)0000-0003-0514-8883 aut Johann, Jerry Adriani (orcid)0000-0001-6184-8011 aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 16(2023), 10 vom: 29. Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:16 year:2023 number:10 day:29 month:09 https://dx.doi.org/10.1007/s12517-023-11688-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2023 10 29 09 |
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10.1007/s12517-023-11688-4 doi (DE-627)SPR053237412 (SPR)s12517-023-11688-4-e DE-627 ger DE-627 rakwb eng Ló, Thiago Berticelli verfasserin (orcid)0000-0002-1457-8810 aut Temporal convolutional neural network for land use and land cover classification using satellite images time series 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences and Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Satellite image time series provide essential information to closely monitor vegetation dynamics, revealing growth pattern and phenological characteristics of crops. In this study, we explored a one-dimension Temporal Convolutional Neural Network (1D-TempCNN) model for land use and land cover classification from Sentinel-2 image time series. The main goal was to evaluate the generalization ability by comparing 1D-TempCNN with two classical methods, Support Vector Machine (SVM) and Random Forest (RF). The experiments were conducted in three different sites, each one with its own crop cycles and time series compositions, and we performed cross-site and cross-year tests. These cross-testing approaches allow examining the ability of the classification models to generalize temporal (and spectral) patterns. Two data augmentation techniques, sliding window and scaling, were used to increase the amount and diversity of data. Our results show that data augmentation technique was essential to handle the data variability, contributing to the generalization of models and overall performance, resulting in an increase of up to 19.72 percentage points in overall accuracy (OA). Additionally, results show that 1D-TempCNN achieved superior OA (94.34–98.67%) in cross-testing, outperforming SVM (88.32–97.45%) and RF (90.25–98.12%). This demonstrates that the proposed 1D-TempCNN model, combined with data augmentation techniques, is capable of higher generalization for land use and land cover classification. Data augmentation (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Sentinel-2 (dpeaa)DE-He213 Cross-site testing (dpeaa)DE-He213 Corrêa, Ulisses Brisolara (orcid)0000-0001-6695-3451 aut Araújo, Ricardo Matsumura (orcid)0000-0003-0514-8883 aut Johann, Jerry Adriani (orcid)0000-0001-6184-8011 aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 16(2023), 10 vom: 29. Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:16 year:2023 number:10 day:29 month:09 https://dx.doi.org/10.1007/s12517-023-11688-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2023 10 29 09 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Satellite image time series provide essential information to closely monitor vegetation dynamics, revealing growth pattern and phenological characteristics of crops. In this study, we explored a one-dimension Temporal Convolutional Neural Network (1D-TempCNN) model for land use and land cover classification from Sentinel-2 image time series. The main goal was to evaluate the generalization ability by comparing 1D-TempCNN with two classical methods, Support Vector Machine (SVM) and Random Forest (RF). The experiments were conducted in three different sites, each one with its own crop cycles and time series compositions, and we performed cross-site and cross-year tests. These cross-testing approaches allow examining the ability of the classification models to generalize temporal (and spectral) patterns. Two data augmentation techniques, sliding window and scaling, were used to increase the amount and diversity of data. Our results show that data augmentation technique was essential to handle the data variability, contributing to the generalization of models and overall performance, resulting in an increase of up to 19.72 percentage points in overall accuracy (OA). Additionally, results show that 1D-TempCNN achieved superior OA (94.34–98.67%) in cross-testing, outperforming SVM (88.32–97.45%) and RF (90.25–98.12%). 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Ló, Thiago Berticelli |
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Ló, Thiago Berticelli misc Data augmentation misc Remote sensing misc Sentinel-2 misc Cross-site testing Temporal convolutional neural network for land use and land cover classification using satellite images time series |
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Temporal convolutional neural network for land use and land cover classification using satellite images time series Data augmentation (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Sentinel-2 (dpeaa)DE-He213 Cross-site testing (dpeaa)DE-He213 |
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Temporal convolutional neural network for land use and land cover classification using satellite images time series |
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Temporal convolutional neural network for land use and land cover classification using satellite images time series |
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temporal convolutional neural network for land use and land cover classification using satellite images time series |
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Temporal convolutional neural network for land use and land cover classification using satellite images time series |
| abstract |
Abstract Satellite image time series provide essential information to closely monitor vegetation dynamics, revealing growth pattern and phenological characteristics of crops. In this study, we explored a one-dimension Temporal Convolutional Neural Network (1D-TempCNN) model for land use and land cover classification from Sentinel-2 image time series. The main goal was to evaluate the generalization ability by comparing 1D-TempCNN with two classical methods, Support Vector Machine (SVM) and Random Forest (RF). The experiments were conducted in three different sites, each one with its own crop cycles and time series compositions, and we performed cross-site and cross-year tests. These cross-testing approaches allow examining the ability of the classification models to generalize temporal (and spectral) patterns. Two data augmentation techniques, sliding window and scaling, were used to increase the amount and diversity of data. Our results show that data augmentation technique was essential to handle the data variability, contributing to the generalization of models and overall performance, resulting in an increase of up to 19.72 percentage points in overall accuracy (OA). Additionally, results show that 1D-TempCNN achieved superior OA (94.34–98.67%) in cross-testing, outperforming SVM (88.32–97.45%) and RF (90.25–98.12%). This demonstrates that the proposed 1D-TempCNN model, combined with data augmentation techniques, is capable of higher generalization for land use and land cover classification. © Saudi Society for Geosciences and Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Satellite image time series provide essential information to closely monitor vegetation dynamics, revealing growth pattern and phenological characteristics of crops. In this study, we explored a one-dimension Temporal Convolutional Neural Network (1D-TempCNN) model for land use and land cover classification from Sentinel-2 image time series. The main goal was to evaluate the generalization ability by comparing 1D-TempCNN with two classical methods, Support Vector Machine (SVM) and Random Forest (RF). The experiments were conducted in three different sites, each one with its own crop cycles and time series compositions, and we performed cross-site and cross-year tests. These cross-testing approaches allow examining the ability of the classification models to generalize temporal (and spectral) patterns. Two data augmentation techniques, sliding window and scaling, were used to increase the amount and diversity of data. Our results show that data augmentation technique was essential to handle the data variability, contributing to the generalization of models and overall performance, resulting in an increase of up to 19.72 percentage points in overall accuracy (OA). Additionally, results show that 1D-TempCNN achieved superior OA (94.34–98.67%) in cross-testing, outperforming SVM (88.32–97.45%) and RF (90.25–98.12%). This demonstrates that the proposed 1D-TempCNN model, combined with data augmentation techniques, is capable of higher generalization for land use and land cover classification. © Saudi Society for Geosciences and Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Satellite image time series provide essential information to closely monitor vegetation dynamics, revealing growth pattern and phenological characteristics of crops. In this study, we explored a one-dimension Temporal Convolutional Neural Network (1D-TempCNN) model for land use and land cover classification from Sentinel-2 image time series. The main goal was to evaluate the generalization ability by comparing 1D-TempCNN with two classical methods, Support Vector Machine (SVM) and Random Forest (RF). The experiments were conducted in three different sites, each one with its own crop cycles and time series compositions, and we performed cross-site and cross-year tests. These cross-testing approaches allow examining the ability of the classification models to generalize temporal (and spectral) patterns. Two data augmentation techniques, sliding window and scaling, were used to increase the amount and diversity of data. Our results show that data augmentation technique was essential to handle the data variability, contributing to the generalization of models and overall performance, resulting in an increase of up to 19.72 percentage points in overall accuracy (OA). Additionally, results show that 1D-TempCNN achieved superior OA (94.34–98.67%) in cross-testing, outperforming SVM (88.32–97.45%) and RF (90.25–98.12%). This demonstrates that the proposed 1D-TempCNN model, combined with data augmentation techniques, is capable of higher generalization for land use and land cover classification. © Saudi Society for Geosciences and Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Temporal convolutional neural network for land use and land cover classification using satellite images time series |
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https://dx.doi.org/10.1007/s12517-023-11688-4 |
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| author2 |
Corrêa, Ulisses Brisolara Araújo, Ricardo Matsumura Johann, Jerry Adriani |
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Corrêa, Ulisses Brisolara Araújo, Ricardo Matsumura Johann, Jerry Adriani |
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| doi_str |
10.1007/s12517-023-11688-4 |
| up_date |
2024-07-03T18:03:43.648Z |
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| score |
7.3988447 |