Hybrid hesitant fuzzy linguistic bi-objective binary coyote clustering based segmentation and classification for land use land cover in hyperspectral image
Land use and land cover segmentation and classification from hyperspectral image is significant in many land use inventories. Though several techniques have been proposed for classification of land use and land cover, the classification accuracy is low in the prevailing approaches. This manuscript o...
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| Vydáno v: | International journal of information technology (Singapore. Online) Ročník 16; číslo 1; s. 525 - 534 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Springer Nature Singapore
01.01.2024
Springer Nature B.V |
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| ISSN: | 2511-2104, 2511-2112 |
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| Abstract | Land use and land cover segmentation and classification from hyperspectral image is significant in many land use inventories. Though several techniques have been proposed for classification of land use and land cover, the classification accuracy is low in the prevailing approaches. This manuscript offers Auto-Metric Graph Neural Network to improve the Land Use/Land Cover (AMGNN-LU/LC) classification. Land use and land cover classification is assessed with the help of EuroSAT dataset. The input image is enhanced with preprocessing method called Anisotropic Diffusion Kuwahara Filtering (ADKF). After preprocessing, Hesitant Fuzzy Linguistic Bi-objective Clustering (HFLBC) technique is utilized for segmentation. The Binary Coyote Optimization Algorithm (BCOA) is used for optimizing hesitant fuzzy linguistic bi-objective clustering. The segmented image is classified into land use/ land cover with the help of auto-metric graph neural network. The introduced method is implemented in PYTHON. The proposed approach attains 99% precision, 98.30% accuracy, 99% F1-score, 99.2% recall, 98.1% sensitivity, 99% specificity, 98% Structural Similarity Index Measure (SSIM), 44 dB Peak Signal-to-Noise Ratio (PSNR) and 2.7 s computational time and 0.12% loss. The AMGNN-LU/LC’s efficiency is compared to the prevailing approaches. |
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| AbstractList | Land use and land cover segmentation and classification from hyperspectral image is significant in many land use inventories. Though several techniques have been proposed for classification of land use and land cover, the classification accuracy is low in the prevailing approaches. This manuscript offers Auto-Metric Graph Neural Network to improve the Land Use/Land Cover (AMGNN-LU/LC) classification. Land use and land cover classification is assessed with the help of EuroSAT dataset. The input image is enhanced with preprocessing method called Anisotropic Diffusion Kuwahara Filtering (ADKF). After preprocessing, Hesitant Fuzzy Linguistic Bi-objective Clustering (HFLBC) technique is utilized for segmentation. The Binary Coyote Optimization Algorithm (BCOA) is used for optimizing hesitant fuzzy linguistic bi-objective clustering. The segmented image is classified into land use/ land cover with the help of auto-metric graph neural network. The introduced method is implemented in PYTHON. The proposed approach attains 99% precision, 98.30% accuracy, 99% F1-score, 99.2% recall, 98.1% sensitivity, 99% specificity, 98% Structural Similarity Index Measure (SSIM), 44 dB Peak Signal-to-Noise Ratio (PSNR) and 2.7 s computational time and 0.12% loss. The AMGNN-LU/LC’s efficiency is compared to the prevailing approaches. Land use and land cover segmentation and classification from hyperspectral image is significant in many land use inventories. Though several techniques have been proposed for classification of land use and land cover, the classification accuracy is low in the prevailing approaches. This manuscript offers Auto-Metric Graph Neural Network to improve the Land Use/Land Cover (AMGNN-LU/LC) classification. Land use and land cover classification is assessed with the help of EuroSAT dataset. The input image is enhanced with preprocessing method called Anisotropic Diffusion Kuwahara Filtering (ADKF). After preprocessing, Hesitant Fuzzy Linguistic Bi-objective Clustering (HFLBC) technique is utilized for segmentation. The Binary Coyote Optimization Algorithm (BCOA) is used for optimizing hesitant fuzzy linguistic bi-objective clustering. The segmented image is classified into land use/ land cover with the help of auto-metric graph neural network. The introduced method is implemented in PYTHON. The proposed approach attains 99% precision, 98.30% accuracy, 99% F1-score, 99.2% recall, 98.1% sensitivity, 99% specificity, 98% Structural Similarity Index Measure (SSIM), 44 dB Peak Signal-to-Noise Ratio (PSNR) and 2.7 s computational time and 0.12% loss. The AMGNN-LU/LC’s efficiency is compared to the prevailing approaches. |
| Author | Sedamkar, R. R. Alegavi, Sujata Yele, Vijaykumar P. |
| Author_xml | – sequence: 1 givenname: Vijaykumar P. orcidid: 0009-0004-5916-4154 surname: Yele fullname: Yele, Vijaykumar P. email: vijaypyele@gmail.com organization: Department of Electronics and Telecommunication Engineering, Thakur College of Engineering and Technology – sequence: 2 givenname: Sujata surname: Alegavi fullname: Alegavi, Sujata organization: Department of Internet of Things, Thakur College of Engineering and Technology – sequence: 3 givenname: R. R. surname: Sedamkar fullname: Sedamkar, R. R. organization: Department of Computer Engineering, Thakur College of Engineering and Technology |
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| Cites_doi | 10.1016/j.heliyon.2021.e07673 10.3390/rs13081433 10.1080/01431161.2020.1871100 10.1007/s10596-020-10030-1 10.3390/ijerph19137990 10.1080/10106049.2021.1917005 10.1016/j.envc.2021.100251 10.1080/10106049.2022.2115153 10.1016/j.ecolind.2021.107540 10.3390/su13063473 10.1109/LGRS.2023.3251652 10.3390/app11020543 10.1016/j.eswa.2020.114355 10.1016/j.scitotenv.2021.145815 10.1016/j.ecolind.2021.107612 10.1109/JBHI.2021.3053568 10.1016/j.ecoser.2021.101338 10.3390/rs12244135 10.1016/j.jhydrol.2021.126872 10.1016/j.patcog.2020.107470 10.1016/j.ecolind.2021.108328 10.3390/math9222984 10.1007/s11042-021-10991-0 10.1016/j.isprsjprs.2021.04.022 10.1007/s11227-020-03547-w 10.3390/app13010034 10.3390/rs13214351 10.1016/j.asoc.2021.107758 |
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| Keywords | Land use and land cover segmentation Anisotropic diffusion Kuwahara filtering Land use and land cover classification Hyperspectral image Binary coyote optimization algorithm |
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| References | Fryer, Williams (CR14) 2021; 775 Pan, Wang, Gao, Dang, Han (CR22) 2022; 37 Soni, Rajpal, Mehta, Mishra (CR18) 2021; 81 Talukdar, Eibek, Akhter, Ziaul, Islam, Mallick (CR20) 2021; 126 Gabriels, Willems, Van Orshoven (CR3) 2021; 602 Zhang, Chen, Lv, Zhang (CR19) 2021; 112 Li, Wu, Gao, Zheng, Wu, Li (CR4) 2021; 132 Shetty, Gupta, Belgiu, Srivastav (CR24) 2021; 13 Nasir, Khan, Varlamis (CR8) 2021; 1 Tolessa, Kidane, Bezie (CR17) 2021; 7 Rostami, Kaveh (CR21) 2021; 25 Song, Mao, Qian (CR35) 2021; 25 Wu, Lin, Xing, Song, Li (CR23) 2021; 103 CR31 Lai, Huang, Chen, Lin, Lin, Lyu (CR5) 2021; 13 Fu, Zhang, Meng, Zhang, Zhang (CR29) 2022; 13 Agrawal, Chowdhary, Agarwala, Mayya, Kamath (CR12) 2022; 14 de Souza, de Macedo, dos Santos, Pierezan, Mariani (CR34) 2020; 107 Anandhalli, Tanuja, Baligar, Baligar (CR10) 2022; 14 Joshi, Alenezi, Thirumoorthy, Dutta, You (CR16) 2021; 9 Thiagarajan, ManapakkamAnandan, Stateczny, BidareDivakarachari, KivudujogappaLingappa (CR28) 2021; 13 Rajendran, Kumarasamy, Zarro, Divakarachari, Ullo (CR26) 2020; 12 Samal, Gedam (CR7) 2021; 5 Zhang, Su, Xu, Luo, Li (CR1) 2021; 11 Xu, Su, Zhang (CR30) 2021; 42 Gopi, Jyothi, Narayana, Sandeep (CR11) 2023; 15 Chetty, Yamin, White (CR9) 2022; 14 Wang, Han (CR2) 2021; 125 Xiao, Xu, Zhao (CR13) 2022; 19 Abera, Tamene, Kassawmar, Mulatu, Kassa, Verchot, Quintero (CR15) 2021; 50 Xu, Li, Chen, He, Li, Mu, Elumalai (CR25) 2022; 37 Zheng, Xu, He, Tian (CR33) 2021; 168 Yang, Rottensteiner, Heipke (CR6) 2021; 177 Temenos, Temenos, Kaselimi, Doulamis, Doulamis (CR27) 2023; 20 Kumar, Poornima, Nagendraswamy, Manjunath (CR32) 2021; 77 W Abera (1576_CR15) 2021; 50 Y Lai (1576_CR5) 2021; 13 MP Kumar (1576_CR32) 2021; 77 G Chetty (1576_CR9) 2022; 14 S Talukdar (1576_CR20) 2021; 126 K Gabriels (1576_CR3) 2021; 602 GB Rajendran (1576_CR26) 2020; 12 K Thiagarajan (1576_CR28) 2021; 13 T Tolessa (1576_CR17) 2021; 7 C Li (1576_CR4) 2021; 132 Y Zhang (1576_CR19) 2021; 112 RC de Souza (1576_CR34) 2020; 107 P Fu (1576_CR29) 2022; 13 F Xu (1576_CR25) 2022; 37 X Pan (1576_CR22) 2022; 37 AP Gopi (1576_CR11) 2023; 15 PK Soni (1576_CR18) 2021; 81 A Temenos (1576_CR27) 2023; 20 1576_CR31 C Yang (1576_CR6) 2021; 177 S Agrawal (1576_CR12) 2022; 14 Y Zheng (1576_CR33) 2021; 168 DR Samal (1576_CR7) 2021; 5 JA Nasir (1576_CR8) 2021; 1 GP Joshi (1576_CR16) 2021; 9 H Wu (1576_CR23) 2021; 103 J Fryer (1576_CR14) 2021; 775 M Anandhalli (1576_CR10) 2022; 14 Z Xu (1576_CR30) 2021; 42 X Song (1576_CR35) 2021; 25 G Wang (1576_CR2) 2021; 125 S Shetty (1576_CR24) 2021; 13 T Zhang (1576_CR1) 2021; 11 P Xiao (1576_CR13) 2022; 19 O Rostami (1576_CR21) 2021; 25 |
| References_xml | – volume: 14 start-page: 95 issue: 1 year: 2022 end-page: 103 ident: CR9 article-title: A low resource 3D U-net based deep learning model for medical image analysis publication-title: Int J Inf Technol – volume: 7 issue: 7 year: 2021 ident: CR17 article-title: Assessment of the linkages between ecosystem service provision and land use/land cover change in Fincha watershed, North-Western Ethiopia publication-title: Heliyon doi: 10.1016/j.heliyon.2021.e07673 – volume: 13 start-page: 1433 issue: 8 year: 2021 ident: CR24 article-title: Assessing the effect of training sampling design on the performance of machine learning classifiers for land cover mapping using multi-temporal remote sensing data and google earth engine publication-title: Remote Sens doi: 10.3390/rs13081433 – volume: 42 start-page: 3146 issue: 8 year: 2021 end-page: 3165 ident: CR30 article-title: A semantic segmentation method with category boundary for land use and land cover (LULC) mapping of very-high resolution (VHR) remote sensing image publication-title: Int J Remote Sens doi: 10.1080/01431161.2020.1871100 – volume: 25 start-page: 911 year: 2021 end-page: 930 ident: CR21 article-title: Optimal feature selection for SAR image classification using biogeography-based optimization (BBO), artificial bee colony (ABC) and support vector machine (SVM): a combined approach of optimization and machine learning publication-title: Comput Geosci doi: 10.1007/s10596-020-10030-1 – volume: 19 start-page: 7990 issue: 13 year: 2022 ident: CR13 article-title: Conflict identification and zoning optimization of “production-living-ecological” space publication-title: Int J Environ Res Public Health doi: 10.3390/ijerph19137990 – volume: 37 start-page: 5415 issue: 18 year: 2022 end-page: 5432 ident: CR22 article-title: Detailed and automated classification of land use/land cover using machine learning algorithms in google earth engine publication-title: Geocarto Int doi: 10.1080/10106049.2021.1917005 – volume: 5 year: 2021 ident: CR7 article-title: Assessing the impacts of land use and land cover change on water resources in the Upper Bhima river basin, India publication-title: Environ Chall doi: 10.1016/j.envc.2021.100251 – volume: 37 start-page: 16769 issue: 27 year: 2022 end-page: 16785 ident: CR25 article-title: Impacts of land use/land cover patterns on groundwater quality in the Guanzhong Basin of northwest China publication-title: Geocarto Int doi: 10.1080/10106049.2022.2115153 – volume: 125 year: 2021 ident: CR2 article-title: The multi-objective spatial optimization of urban land use based on low-carbon city planning publication-title: Ecol Indic doi: 10.1016/j.ecolind.2021.107540 – volume: 13 start-page: 3473 issue: 6 year: 2021 ident: CR5 article-title: Land use dynamics and optimization from 2000 to 2020 in East Guangdong Province, China publication-title: Sustainability doi: 10.3390/su13063473 – volume: 20 start-page: 1 year: 2023 end-page: 5 ident: CR27 article-title: Interpretable deep learning framework for land use and land cover classification in remote sensing using SHAP publication-title: IEEE Geosci Remote Sens Lett doi: 10.1109/LGRS.2023.3251652 – volume: 11 start-page: 543 issue: 2 year: 2021 ident: CR1 article-title: Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier publication-title: Appl Sci doi: 10.3390/app11020543 – volume: 168 year: 2021 ident: CR33 article-title: A hesitant fuzzy linguistic bi-objective clustering method for large-scale group decision-making publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2020.114355 – volume: 775 year: 2021 ident: CR14 article-title: Regional carbon stock assessment and the potential effects of land cover change publication-title: Sci Total Environ doi: 10.1016/j.scitotenv.2021.145815 – volume: 14 start-page: 3343 issue: 7 year: 2022 end-page: 3353 ident: CR10 article-title: Indian pothole detection based on CNN and anchor-based deep learning method publication-title: Int J Inf Technol – ident: CR31 – volume: 126 year: 2021 ident: CR20 article-title: Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh publication-title: Ecol Indic doi: 10.1016/j.ecolind.2021.107612 – volume: 25 start-page: 3141 issue: 8 year: 2021 end-page: 3152 ident: CR35 article-title: Auto-metric graph neural network based on a meta-learning strategy for the diagnosis of Alzheimer's disease publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2021.3053568 – volume: 50 year: 2021 ident: CR15 article-title: Impacts of land use and land cover dynamics on ecosystem services in the Yayo coffee forest biosphere reserve, southwestern Ethiopia publication-title: Ecosyst Serv doi: 10.1016/j.ecoser.2021.101338 – volume: 12 start-page: 4135 issue: 24 year: 2020 ident: CR26 article-title: Land-use and land-cover classification using a human group-based particle swarm optimization algorithm with an LSTM classifier on hybrid pre-processing remote-sensing images publication-title: Remote Sens doi: 10.3390/rs12244135 – volume: 1 issue: 1 year: 2021 ident: CR8 article-title: Fake news detection: a hybrid CNN-RNN based deep learning approach publication-title: Int J Inf Manag Data Insights – volume: 602 year: 2021 ident: CR3 article-title: Performance evaluation of spatially distributed, CN-based rainfall-runoff model configurations for implementation in spatial land use optimization analyses publication-title: J Hydrol doi: 10.1016/j.jhydrol.2021.126872 – volume: 107 year: 2020 ident: CR34 article-title: Binary coyote optimization algorithm for feature selection publication-title: Pattern Recognit doi: 10.1016/j.patcog.2020.107470 – volume: 132 year: 2021 ident: CR4 article-title: Multi-scenario simulation of ecosystem service value for optimization of land use in the Sichuan-Yunnan ecological barrier, China publication-title: Ecol Indic doi: 10.1016/j.ecolind.2021.108328 – volume: 15 start-page: 965 issue: 2 year: 2023 end-page: 980 ident: CR11 article-title: Classification of tweets data based on polarity using improved RBF kernel of SVM publication-title: Int J Inf Technol – volume: 9 start-page: 2984 issue: 22 year: 2021 ident: CR16 article-title: Ensemble of deep learning-based multimodal remote sensing image classification model on unmanned aerial vehicle networks publication-title: Mathematics doi: 10.3390/math9222984 – volume: 81 start-page: 36853 year: 2021 end-page: 36867 ident: CR18 article-title: Urban land cover and land use classification using multispectral sentinal-2 imagery publication-title: Multimed Tools Appl doi: 10.1007/s11042-021-10991-0 – volume: 177 start-page: 38 year: 2021 end-page: 56 ident: CR6 article-title: A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases publication-title: ISPRS J Photogramm Remote Sens doi: 10.1016/j.isprsjprs.2021.04.022 – volume: 77 start-page: 8445 year: 2021 end-page: 8513 ident: CR32 article-title: Structure-preserving NPR framework for image abstraction and stylization publication-title: J Supercomput doi: 10.1007/s11227-020-03547-w – volume: 13 start-page: 34 issue: 1 year: 2022 ident: CR29 article-title: HGF spatial-spectral fusion method for hyperspectral images publication-title: Appl Sci doi: 10.3390/app13010034 – volume: 103 year: 2021 ident: CR23 article-title: Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method publication-title: Int J Appl Earth Obs Geoinf – volume: 13 start-page: 4351 issue: 21 year: 2021 ident: CR28 article-title: Satellite image classification using a hierarchical ensemble learning and correlation coefficient-based gravitational search algorithm publication-title: Remote Sens doi: 10.3390/rs13214351 – volume: 112 year: 2021 ident: CR19 article-title: Optimization of urban heat effect mitigation based on multi-type ant colony algorithm publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2021.107758 – volume: 14 start-page: 3619 issue: 7 year: 2022 end-page: 3627 ident: CR12 article-title: Content-based medical image retrieval system for lung diseases using deep CNNs publication-title: Int J Inf Technol – volume: 9 start-page: 2984 issue: 22 year: 2021 ident: 1576_CR16 publication-title: Mathematics doi: 10.3390/math9222984 – volume: 13 start-page: 1433 issue: 8 year: 2021 ident: 1576_CR24 publication-title: Remote Sens doi: 10.3390/rs13081433 – volume: 13 start-page: 4351 issue: 21 year: 2021 ident: 1576_CR28 publication-title: Remote Sens doi: 10.3390/rs13214351 – volume: 13 start-page: 34 issue: 1 year: 2022 ident: 1576_CR29 publication-title: Appl Sci doi: 10.3390/app13010034 – volume: 14 start-page: 95 issue: 1 year: 2022 ident: 1576_CR9 publication-title: Int J Inf Technol – volume: 25 start-page: 911 year: 2021 ident: 1576_CR21 publication-title: Comput Geosci doi: 10.1007/s10596-020-10030-1 – volume: 1 issue: 1 year: 2021 ident: 1576_CR8 publication-title: Int J Inf Manag Data Insights – volume: 177 start-page: 38 year: 2021 ident: 1576_CR6 publication-title: ISPRS J Photogramm Remote Sens doi: 10.1016/j.isprsjprs.2021.04.022 – volume: 13 start-page: 3473 issue: 6 year: 2021 ident: 1576_CR5 publication-title: Sustainability doi: 10.3390/su13063473 – volume: 103 year: 2021 ident: 1576_CR23 publication-title: Int J Appl Earth Obs Geoinf – volume: 11 start-page: 543 issue: 2 year: 2021 ident: 1576_CR1 publication-title: Appl Sci doi: 10.3390/app11020543 – volume: 132 year: 2021 ident: 1576_CR4 publication-title: Ecol Indic doi: 10.1016/j.ecolind.2021.108328 – volume: 5 year: 2021 ident: 1576_CR7 publication-title: Environ Chall doi: 10.1016/j.envc.2021.100251 – volume: 19 start-page: 7990 issue: 13 year: 2022 ident: 1576_CR13 publication-title: Int J Environ Res Public Health doi: 10.3390/ijerph19137990 – volume: 42 start-page: 3146 issue: 8 year: 2021 ident: 1576_CR30 publication-title: Int J Remote Sens doi: 10.1080/01431161.2020.1871100 – volume: 14 start-page: 3619 issue: 7 year: 2022 ident: 1576_CR12 publication-title: Int J Inf Technol – volume: 14 start-page: 3343 issue: 7 year: 2022 ident: 1576_CR10 publication-title: Int J Inf Technol – volume: 20 start-page: 1 year: 2023 ident: 1576_CR27 publication-title: IEEE Geosci Remote Sens Lett doi: 10.1109/LGRS.2023.3251652 – volume: 602 year: 2021 ident: 1576_CR3 publication-title: J Hydrol doi: 10.1016/j.jhydrol.2021.126872 – volume: 15 start-page: 965 issue: 2 year: 2023 ident: 1576_CR11 publication-title: Int J Inf Technol – volume: 168 year: 2021 ident: 1576_CR33 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2020.114355 – volume: 112 year: 2021 ident: 1576_CR19 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2021.107758 – volume: 125 year: 2021 ident: 1576_CR2 publication-title: Ecol Indic doi: 10.1016/j.ecolind.2021.107540 – volume: 37 start-page: 5415 issue: 18 year: 2022 ident: 1576_CR22 publication-title: Geocarto Int doi: 10.1080/10106049.2021.1917005 – volume: 775 year: 2021 ident: 1576_CR14 publication-title: Sci Total Environ doi: 10.1016/j.scitotenv.2021.145815 – volume: 81 start-page: 36853 year: 2021 ident: 1576_CR18 publication-title: Multimed Tools Appl doi: 10.1007/s11042-021-10991-0 – volume: 107 year: 2020 ident: 1576_CR34 publication-title: Pattern Recognit doi: 10.1016/j.patcog.2020.107470 – volume: 50 year: 2021 ident: 1576_CR15 publication-title: Ecosyst Serv doi: 10.1016/j.ecoser.2021.101338 – volume: 37 start-page: 16769 issue: 27 year: 2022 ident: 1576_CR25 publication-title: Geocarto Int doi: 10.1080/10106049.2022.2115153 – volume: 12 start-page: 4135 issue: 24 year: 2020 ident: 1576_CR26 publication-title: Remote Sens doi: 10.3390/rs12244135 – volume: 126 year: 2021 ident: 1576_CR20 publication-title: Ecol Indic doi: 10.1016/j.ecolind.2021.107612 – ident: 1576_CR31 – volume: 25 start-page: 3141 issue: 8 year: 2021 ident: 1576_CR35 publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2021.3053568 – volume: 77 start-page: 8445 year: 2021 ident: 1576_CR32 publication-title: J Supercomput doi: 10.1007/s11227-020-03547-w – volume: 7 issue: 7 year: 2021 ident: 1576_CR17 publication-title: Heliyon doi: 10.1016/j.heliyon.2021.e07673 |
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| SubjectTerms | Accuracy Agriculture Algorithms Artificial Intelligence Biodiversity Classification Climate change Clustering Computer Imaging Computer Science Computing time Datasets Decision making Deep learning Food security Graph neural networks Hyperspectral imaging Image classification Image Processing and Computer Vision Image segmentation Infrastructure Land cover Land use Linguistics Machine Learning Natural resource management Neural networks Noise levels Objectives Optimization Original Research Pattern Recognition and Graphics Preprocessing Remote sensing Segmentation Semantics Signal to noise ratio Software Engineering Urban planning Vegetation Vision |
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| Title | Hybrid hesitant fuzzy linguistic bi-objective binary coyote clustering based segmentation and classification for land use land cover in hyperspectral image |
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