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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-09-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2022.2081538 |
| _version_ | 1797661138314330112 |
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| author | José Escorcia-Gutierrez Margarita Gamarra Melitsa Torres-Torres Natasha Madera Juan C. Calabria-Sarmiento Romany F. Mansour |
| author_facet | José Escorcia-Gutierrez Margarita Gamarra Melitsa Torres-Torres Natasha Madera Juan C. Calabria-Sarmiento Romany F. Mansour |
| author_sort | José Escorcia-Gutierrez |
| collection | DOAJ |
| 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%. |
| first_indexed | 2024-03-11T18:40:05Z |
| format | Article |
| id | doaj.art-b562f72cfc464c649ad7b15a5e3c8284 |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:40:05Z |
| publishDate | 2022-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-b562f72cfc464c649ad7b15a5e3c82842023-10-12T13:36:24ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712022-09-0148562163210.1080/07038992.2022.20815382081538Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crops Type Classification Using Hyperspectral ImagesJosé Escorcia-Gutierrez0Margarita Gamarra1Melitsa Torres-Torres2Natasha Madera3Juan C. Calabria-Sarmiento4Romany F. Mansour5Biomedical Engineering Program, Corporación Universitaria ReformadaDepartament of Computational Science and Electronic, Universidad de la Costa, CUCResearch Group IET-UAC, Universidad Autónoma del CaribeElectronics and Telecommunications Engineering Program, Universidad Autónoma del CaribeDepartment of Computer Science, Universidad Simon BolivarDepartment of Mathematics, Faculty of Science, New Valley UniversityHyperspectral 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%.http://dx.doi.org/10.1080/07038992.2022.2081538 |
| spellingShingle | José Escorcia-Gutierrez Margarita Gamarra Melitsa Torres-Torres Natasha Madera Juan C. Calabria-Sarmiento Romany F. Mansour Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crops Type Classification Using Hyperspectral Images Canadian Journal of Remote Sensing |
| title | 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_short | 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 |
| url | http://dx.doi.org/10.1080/07038992.2022.2081538 |
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