Intelligent sine cosine optimization with deep transfer learning based crops type classification using hyperspectral images
Hyperspectral Remote Sensing (HRS) is an emergent, multidisciplinary paradigm with several applications, which are developed on the basis of material spectroscopy, radiative transfer, and imaging spectroscopy. HRS plays a vital role in agriculture for crops type classification and soil prediction. T...
- Autores:
-
Escorcia-Gutierrez, Jose
Gamarra, Margarita
Torres Torres, Melitsa
Madera, Natasha
Calabria- Sarmiento, Juan Carlos
Mansour, Romany F.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9383
- Acceso en línea:
- https://hdl.handle.net/11323/9383
https://doi.org/10.1080/07038992.2022.2081538
https://repositorio.cuc.edu.co/
- Palabra clave:
- Hyperspectral Remote Sensing (HRS)
Material spectroscopy
Agriculture
- Rights
- embargoedAccess
- License
- © 2022 Informa UK Limited
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Intelligent sine cosine optimization with deep transfer learning based crops type classification using hyperspectral images |
| dc.title.translated.eng.fl_str_mv |
Optimisation intelligente du sinus-cosinus et classification des types de cultures basée sur l'apprentissage par transfert profond à l’aide d’images hyperspectrales |
| title |
Intelligent sine cosine optimization with deep transfer learning based crops type classification using hyperspectral images |
| spellingShingle |
Intelligent sine cosine optimization with deep transfer learning based crops type classification using hyperspectral images Hyperspectral Remote Sensing (HRS) Material spectroscopy Agriculture |
| title_short |
Intelligent sine cosine optimization with deep transfer learning based crops type classification using hyperspectral images |
| title_full |
Intelligent sine cosine optimization with deep transfer learning based crops type classification using hyperspectral images |
| title_fullStr |
Intelligent sine cosine optimization with deep transfer learning based crops type classification using hyperspectral images |
| title_full_unstemmed |
Intelligent sine cosine optimization with deep transfer learning based crops type classification using hyperspectral images |
| title_sort |
Intelligent sine cosine optimization with deep transfer learning based crops type classification using hyperspectral images |
| dc.creator.fl_str_mv |
Escorcia-Gutierrez, Jose Gamarra, Margarita Torres Torres, Melitsa Madera, Natasha Calabria- Sarmiento, Juan Carlos Mansour, Romany F. |
| dc.contributor.author.spa.fl_str_mv |
Escorcia-Gutierrez, Jose Gamarra, Margarita Torres Torres, Melitsa Madera, Natasha Calabria- Sarmiento, Juan Carlos Mansour, Romany F. |
| dc.subject.proposal.eng.fl_str_mv |
Hyperspectral Remote Sensing (HRS) Material spectroscopy Agriculture |
| topic |
Hyperspectral Remote Sensing (HRS) Material spectroscopy Agriculture |
| description |
Hyperspectral Remote Sensing (HRS) is an emergent, multidisciplinary paradigm with several applications, which are developed on the basis of material spectroscopy, radiative transfer, and imaging spectroscopy. HRS plays a vital role in agriculture for crops type classification and soil prediction. The recently developed artificial intelligence techniques can be used for crops type classification using HRS. This study develops an Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crop Type Classification (ISCO-DTLCTC) model. The ISCO-DTLCTC technique comprises initial preprocessing step to extract the region of interest. The information gain-based feature reduction technique is employed to reduce the dimensionality of the original hyperspectral images. In addition, a fusion of 3 deep convolutional neural networks models namely, VGG16, SqueezeNet, and Dense-EfficientNet perform feature extraction process. Furthermore, sine cosine optimization (SCO) algorithm with Modified Elman Neural Network (MENN) model is applied for crops type classification. The design of SCO algorithm helps to proficiently select the parameters involved in the MENN model. The performance validation of the ISCO-DTLCTC model is carried out using benchmark datasets and the results are inspected under several measures. Extensive comparative results demonstrated the betterment of the ISCO-DTLCTC model over the state of art approaches with maximum accuracy of 99.99%. |
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2022 |
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2022-07-19T18:34:17Z |
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2022-07-19T18:34:17Z 2023-06-22 |
| dc.date.issued.none.fl_str_mv |
2022-06-22 |
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Artículo de revista |
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José Escorcia-Gutierrez, Margarita Gamarra, Melitsa Torres-Torres, Natasha Madera, Juan C. Calabria-Sarmiento & Romany F. Mansour (2022): Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crops Type Classification Using Hyperspectral Images, Canadian Journal of Remote Sensing, DOI: 10.1080/07038992.2022.2081538 |
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0703-8992 |
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https://hdl.handle.net/11323/9383 |
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https://doi.org/10.1080/07038992.2022.2081538 |
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10.1080/07038992.2022.2081538 |
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1712-7971 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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José Escorcia-Gutierrez, Margarita Gamarra, Melitsa Torres-Torres, Natasha Madera, Juan C. Calabria-Sarmiento & Romany F. Mansour (2022): Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crops Type Classification Using Hyperspectral Images, Canadian Journal of Remote Sensing, DOI: 10.1080/07038992.2022.2081538 0703-8992 10.1080/07038992.2022.2081538 1712-7971 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/9383 https://doi.org/10.1080/07038992.2022.2081538 https://repositorio.cuc.edu.co/ |
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eng |
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Canadian Journal of Remote Sensing |
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1. Abualigah, L., and Diabat, A. 2021. “Advances in sine cosine algorithm: A comprehensive survey.” Artificial Intelligence Review, Vol. 54(No. 4): pp. 2567–2608. doi:https://doi.org/10.1007/s10462-020-09909-3. [Crossref], [Web of Science ®], [Google Scholar] 2. Ang, K.L.-M., and Seng, J.K.P. 2021. “Big data and machine learning with hyperspectral information in agriculture.” IEEE Access, Vol. 9: pp. 36699–36718. doi:https://doi.org/10.1109/ACCESS.2021.3051196. [Crossref], [Google Scholar] 3. Bernardo, L.S., Damaševičius, R., de Albuquerque, V.H.C., and Maskeliūnas, R. 2021. “A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns.” Advanced Machine Learning Techniques in Data Analysis. Vol. 31: pp. 549–561. [Google Scholar] 4. Bhosle, K., and Musande, V. 2019. “Evaluation of deep learning CNN model for land use land cover classification and crop identification using hyperspectral remote sensing images.” Journal of the Indian Society of Remote Sensing, Vol. 47(No. 11): pp. 1949–1958. doi:https://doi.org/10.1007/s12524-019-01041-2. [Crossref], [Web of Science ®], [Google Scholar] 5. Chasmer, L.E., Ryerson, R.A., and Coburn, C.A. 2022. “Educating the next generation of remote sensing specialists: Skills and industry needs in a changing world.” Canadian Journal of Remote Sensing, Vol. 48(No. 1): pp. 55–70. doi:https://doi.org/10.1080/07038992.2021.1925531. [Taylor & Francis Online], [Google Scholar] 6. Jamali, A., Mahdianpari, M., Brisco, B., Granger, J., Mohammadimanesh, F., and Salehi, B. 2021. “Wetland mapping using multi-spectral satellite imagery and deep convolutional neural networks: A case study in Newfoundland and Labrador.” Canadian Journal of Remote Sensing, Vol. 47(No. 2): pp. 243–260. doi:https://doi.org/10.1080/07038992.2021.1901562. [Taylor & Francis Online], [Google Scholar] 7. Kuo, B.-C., Ho, H.-H., Li, C.-H., Hung, C.-C., and Taur, J.-S. 2014. “A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7(No. 1): pp. 317–326. doi:https://doi.org/10.1109/JSTARS.2013.2262926. [Crossref], [Web of Science ®], [Google Scholar] 8. Lassalle, G. 2021. “Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review.” The Science of the Total Environment, Vol. 788(No. September): pp. 147758. doi:https://doi.org/10.1016/j.scitotenv.2021.147758. [Crossref], [PubMed], [Google Scholar] 9. Lu, B., Dao, P., Liu, J., He, Y., and Shang, J. 2020. “Recent advances of hyperspectral imaging technology and applications in agriculture.” Remote Sensing, Vol. 12(No. 16): pp. 2659. doi:https://doi.org/10.3390/rs12162659. [Crossref], [Web of Science ®], [Google Scholar] 10. Luo, F., Du, B., Zhang, L., Zhang, L., and Tao, D. 2019. “Feature learning using spatial-spectral hypergraph discriminant analysis for hyperspectral image.” IEEE Transactions on Cybernetics, Vol. 49(No. 7): pp. 2406–2419. [Crossref], [PubMed], [Web of Science ®], [Google Scholar] 11. 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Escorcia-Gutierrez, JoseGamarra, MargaritaTorres Torres, MelitsaMadera, NatashaCalabria- Sarmiento, Juan CarlosMansour, Romany F.2022-07-19T18:34:17Z2023-06-222022-07-19T18:34:17Z2022-06-22José Escorcia-Gutierrez, Margarita Gamarra, Melitsa Torres-Torres, Natasha Madera, Juan C. Calabria-Sarmiento & Romany F. Mansour (2022): Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crops Type Classification Using Hyperspectral Images, Canadian Journal of Remote Sensing, DOI: 10.1080/07038992.2022.20815380703-8992https://hdl.handle.net/11323/9383https://doi.org/10.1080/07038992.2022.208153810.1080/07038992.2022.20815381712-7971Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Hyperspectral Remote Sensing (HRS) is an emergent, multidisciplinary paradigm with several applications, which are developed on the basis of material spectroscopy, radiative transfer, and imaging spectroscopy. HRS plays a vital role in agriculture for crops type classification and soil prediction. The recently developed artificial intelligence techniques can be used for crops type classification using HRS. This study develops an Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crop Type Classification (ISCO-DTLCTC) model. The ISCO-DTLCTC technique comprises initial preprocessing step to extract the region of interest. The information gain-based feature reduction technique is employed to reduce the dimensionality of the original hyperspectral images. In addition, a fusion of 3 deep convolutional neural networks models namely, VGG16, SqueezeNet, and Dense-EfficientNet perform feature extraction process. Furthermore, sine cosine optimization (SCO) algorithm with Modified Elman Neural Network (MENN) model is applied for crops type classification. The design of SCO algorithm helps to proficiently select the parameters involved in the MENN model. The performance validation of the ISCO-DTLCTC model is carried out using benchmark datasets and the results are inspected under several measures. Extensive comparative results demonstrated the betterment of the ISCO-DTLCTC model over the state of art approaches with maximum accuracy of 99.99%.La télédétection hyperspectrale (HRS) est une technologie émergente et multidisciplinaire ayant plusieurs applications développées sur la base de la spectroscopie des matériaux, du transfert radiatif et de la spectroscopie des images. L’HRS joue un rôle essentiel en agriculture pour la classification des types de cultures et la prévision des sols. Les techniques d’intelligence artificielle (IA) récemment développées peuvent être utilisées pour la classification des types de cultures à l’aide de HRS. Cette étude développe un modèle intelligent d’optimisation du sinus-cosinus avec une classification des types de cultures basée sur l’apprentissage par transfert profond (ISCO-DTLCTC). La technique ISCO-DTLCTC comprend une étape initiale de prétraitement pour extraire la région d’intérêt (RoI). La technique IGFR (Information Gain Based Feature Reduction) est utilisée pour réduire la dimensionnalité des images hyperspectrales originales. Une fusion de trois modèles DCNN (Deep Convolutional Neural Networks), à savoir VGG16, SqueezeNet et Dense-EfficientNet, effectue un processus d’extraction des principales caractéristiques. En outre, l’algorithme d’optimisation du sinus-cosinus (SCO) avec le modèle MENN (Modified Elman Neural Network) est appliqué à la classification des types de cultures. La conception de l’algorithme SCO permet de sélectionner efficacement les paramètres impliqués dans le modèle MENN. La validation des performances du modèle ISCO-DTLCTC est effectuée à l’aide d’ensembles de données de référence et les résultats sont validés avec différents paramètres. Les résultats démontrent l’efficacité du modèle ISCO-DTLCTC par rapport aux approches de pointe avec une précision maximale de 99,99%.13 páginasapplication/pdfengTaylor and Francis Ltd.United Kingdom© 2022 Informa UK LimitedAtribución-NoComercial 4.0 Internacional (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfIntelligent sine cosine optimization with deep transfer learning based crops type classification using hyperspectral imagesOptimisation intelligente du sinus-cosinus et classification des types de cultures basée sur l'apprentissage par transfert profond à l’aide d’images hyperspectralesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://www.tandfonline.com/doi/full/10.1080/07038992.2022.2081538Canadian Journal of Remote Sensing1. Abualigah, L., and Diabat, A. 2021. “Advances in sine cosine algorithm: A comprehensive survey.” Artificial Intelligence Review, Vol. 54(No. 4): pp. 2567–2608. doi:https://doi.org/10.1007/s10462-020-09909-3. [Crossref], [Web of Science ®], [Google Scholar]2. Ang, K.L.-M., and Seng, J.K.P. 2021. “Big data and machine learning with hyperspectral information in agriculture.” IEEE Access, Vol. 9: pp. 36699–36718. doi:https://doi.org/10.1109/ACCESS.2021.3051196. [Crossref], [Google Scholar]3. Bernardo, L.S., Damaševičius, R., de Albuquerque, V.H.C., and Maskeliūnas, R. 2021. “A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns.” Advanced Machine Learning Techniques in Data Analysis. Vol. 31: pp. 549–561. [Google Scholar]4. Bhosle, K., and Musande, V. 2019. “Evaluation of deep learning CNN model for land use land cover classification and crop identification using hyperspectral remote sensing images.” Journal of the Indian Society of Remote Sensing, Vol. 47(No. 11): pp. 1949–1958. doi:https://doi.org/10.1007/s12524-019-01041-2. [Crossref], [Web of Science ®], [Google Scholar]5. Chasmer, L.E., Ryerson, R.A., and Coburn, C.A. 2022. “Educating the next generation of remote sensing specialists: Skills and industry needs in a changing world.” Canadian Journal of Remote Sensing, Vol. 48(No. 1): pp. 55–70. doi:https://doi.org/10.1080/07038992.2021.1925531. [Taylor & Francis Online], [Google Scholar]6. Jamali, A., Mahdianpari, M., Brisco, B., Granger, J., Mohammadimanesh, F., and Salehi, B. 2021. “Wetland mapping using multi-spectral satellite imagery and deep convolutional neural networks: A case study in Newfoundland and Labrador.” Canadian Journal of Remote Sensing, Vol. 47(No. 2): pp. 243–260. doi:https://doi.org/10.1080/07038992.2021.1901562. [Taylor & Francis Online], [Google Scholar]7. Kuo, B.-C., Ho, H.-H., Li, C.-H., Hung, C.-C., and Taur, J.-S. 2014. “A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7(No. 1): pp. 317–326. doi:https://doi.org/10.1109/JSTARS.2013.2262926. [Crossref], [Web of Science ®], [Google Scholar]8. 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