On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network
<p>This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. Tobacco is a major revenue generating crop of Khyber Pakhtunkhwa (KP) province of Pakistan, with over 90% of the country’s Tobacco production. In order to analyze the...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2020
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| _version_ | 1864513512822276096 |
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| author | Nasru Minallah (9427338) |
| author2 | Mohsin Tariq (4266667) Najam Aziz (9427341) Waleed Khan (9427344) Atiq ur Rehman (3044409) Samir Brahim Belhaouari (9427347) |
| author2_role | author author author author author |
| author_facet | Nasru Minallah (9427338) Mohsin Tariq (4266667) Najam Aziz (9427341) Waleed Khan (9427344) Atiq ur Rehman (3044409) Samir Brahim Belhaouari (9427347) |
| author_role | author |
| dc.creator.none.fl_str_mv | Nasru Minallah (9427338) Mohsin Tariq (4266667) Najam Aziz (9427341) Waleed Khan (9427344) Atiq ur Rehman (3044409) Samir Brahim Belhaouari (9427347) |
| dc.date.none.fl_str_mv | 2020-09-28T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0239746 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/On_the_performance_of_fusion_based_planet-scope_and_Sentinel-2_data_for_crop_classification_using_inception_inspired_deep_convolutional_neural_network/25958128 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Crop and pasture production Engineering Geomatic engineering Information and computing sciences Artificial intelligence Machine learning Crops Nicotiana Machine learning Deep learning Artificial neural networks Machine learning algorithms Neural networks Wheat |
| dc.title.none.fl_str_mv | On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. Tobacco is a major revenue generating crop of Khyber Pakhtunkhwa (KP) province of Pakistan, with over 90% of the country’s Tobacco production. In order to analyze the performance of the developed classification framework, a pilot sub-region named Yar Hussain is selected for experimentation work. Yar Hussain is a tehsil of district Swabi, within KP province of Pakistan, having highest contribution to the gross production of the KP Tobacco crop. KP generally consists of a diverse crop land with different varieties of vegetation, having similar phenology which makes crop classification a challenging task. In this study, a temporal convolutional neural network (TempCNNs) model is implemented for crop classification, while considering remotely sensed imagery of the selected pilot region with specific focus on the Tobacco crop. In order to improve the performance of the proposed classification framework, instead of using the prevailing concept of utilizing a single satellite imagery, both Sentinel-2 and Planet-Scope imageries are stacked together to assist in providing more diverse features to the proposed classification framework. Furthermore, instead of using a single date satellite imagery, multiple satellite imageries with respect to the phenological cycle of Tobacco crop are temporally stacked together which resulted in a higher temporal resolution of the employed satellite imagery. The developed framework is trained using the ground truth data. The final output is obtained as an outcome of the SoftMax function of the developed model in the form of probabilistic values, for the classification of the selected classes. The proposed deep learning-based crop classification framework, while utilizing multi-satellite temporally stacked imagery resulted in an overall classification accuracy of 98.15%. Furthermore, as the developed classification framework evolved with specific focus on Tobacco crop, it resulted in best Tobacco crop classification accuracy of 99%.</p><h2>Other Information</h2> <p> Published in: PLOS ONE<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1371/journal.pone.0239746" target="_blank">https://dx.doi.org/10.1371/journal.pone.0239746</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a14ed937be2b0171cc3259a47c42315e |
| identifier_str_mv | 10.1371/journal.pone.0239746 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25958128 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural networkNasru Minallah (9427338)Mohsin Tariq (4266667)Najam Aziz (9427341)Waleed Khan (9427344)Atiq ur Rehman (3044409)Samir Brahim Belhaouari (9427347)Agricultural, veterinary and food sciencesCrop and pasture productionEngineeringGeomatic engineeringInformation and computing sciencesArtificial intelligenceMachine learningCropsNicotianaMachine learningDeep learningArtificial neural networksMachine learning algorithmsNeural networksWheat<p>This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. Tobacco is a major revenue generating crop of Khyber Pakhtunkhwa (KP) province of Pakistan, with over 90% of the country’s Tobacco production. In order to analyze the performance of the developed classification framework, a pilot sub-region named Yar Hussain is selected for experimentation work. Yar Hussain is a tehsil of district Swabi, within KP province of Pakistan, having highest contribution to the gross production of the KP Tobacco crop. KP generally consists of a diverse crop land with different varieties of vegetation, having similar phenology which makes crop classification a challenging task. In this study, a temporal convolutional neural network (TempCNNs) model is implemented for crop classification, while considering remotely sensed imagery of the selected pilot region with specific focus on the Tobacco crop. In order to improve the performance of the proposed classification framework, instead of using the prevailing concept of utilizing a single satellite imagery, both Sentinel-2 and Planet-Scope imageries are stacked together to assist in providing more diverse features to the proposed classification framework. Furthermore, instead of using a single date satellite imagery, multiple satellite imageries with respect to the phenological cycle of Tobacco crop are temporally stacked together which resulted in a higher temporal resolution of the employed satellite imagery. The developed framework is trained using the ground truth data. The final output is obtained as an outcome of the SoftMax function of the developed model in the form of probabilistic values, for the classification of the selected classes. The proposed deep learning-based crop classification framework, while utilizing multi-satellite temporally stacked imagery resulted in an overall classification accuracy of 98.15%. Furthermore, as the developed classification framework evolved with specific focus on Tobacco crop, it resulted in best Tobacco crop classification accuracy of 99%.</p><h2>Other Information</h2> <p> Published in: PLOS ONE<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1371/journal.pone.0239746" target="_blank">https://dx.doi.org/10.1371/journal.pone.0239746</a></p>2020-09-28T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1371/journal.pone.0239746https://figshare.com/articles/journal_contribution/On_the_performance_of_fusion_based_planet-scope_and_Sentinel-2_data_for_crop_classification_using_inception_inspired_deep_convolutional_neural_network/25958128CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/259581282020-09-28T09:00:00Z |
| spellingShingle | On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network Nasru Minallah (9427338) Agricultural, veterinary and food sciences Crop and pasture production Engineering Geomatic engineering Information and computing sciences Artificial intelligence Machine learning Crops Nicotiana Machine learning Deep learning Artificial neural networks Machine learning algorithms Neural networks Wheat |
| status_str | publishedVersion |
| title | On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network |
| title_full | On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network |
| title_fullStr | On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network |
| title_full_unstemmed | On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network |
| title_short | On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network |
| title_sort | On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network |
| topic | Agricultural, veterinary and food sciences Crop and pasture production Engineering Geomatic engineering Information and computing sciences Artificial intelligence Machine learning Crops Nicotiana Machine learning Deep learning Artificial neural networks Machine learning algorithms Neural networks Wheat |