Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging

Abstract In recent years, deep learning techniques have presented a major role in hyperspectral image (HSI) classification. Most commonly Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has greatly advanced the accuracy of hyperspectral image classification, making it powerful t...
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Gespeichert in:

Autor*in:	Dash, Satyabrata [verfasserIn] Chakravarty, Sujata Giri, Nimay Chandra Agyekum, Ephraim Bonah AboRas, Kareem M.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Hyperspectral imaging K-Nearest neighbour support vector machine (SVM) Convolutional neural network (CNN) K-Nearest neighbour algorithm (KNN) Principal component analysis (PCA) Minimum noise fraction (MNF)

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: International journal of computational intelligence systems - Paris : Atlantis Press, 2008, 17(2024), 1 vom: 29. Jan.
Übergeordnetes Werk:	volume:17 ; year:2024 ; number:1 ; day:29 ; month:01

Links:	Volltext

DOI / URN:	10.1007/s44196-023-00370-y

Katalog-ID:	SPR054573068

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allfields	10.1007/s44196-023-00370-y doi (DE-627)SPR054573068 (SPR)s44196-023-00370-y-e DE-627 ger DE-627 rakwb eng Dash, Satyabrata verfasserin aut Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract In recent years, deep learning techniques have presented a major role in hyperspectral image (HSI) classification. Most commonly Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has greatly advanced the accuracy of hyperspectral image classification, making it powerful tool for remote sensing applications. Deep structure learning, which involves multiple layers of neural network, has shown promising results in effectively addressing nonlinear problems and improving classification accuracy and reduce execution time. The exact categorization of ground topographies from hyperspectral data is a crucial and current research topic that has gotten a lot of attention. This research work focuses on hyperspectral image categorization utilizing several machine learning approaches such as support vector machine (SVM), K-Nearest Neighbour (KNN), CNN and LSTM. To reduce the number of superfluous and noisy bands in the dataset, Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) were utilized. Different performance evaluation measures like time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and Gmean have been taken to prove the efficacy of the models. Based on the simulation results, it is observed that the LSTM model outperforms the other models in terms of accuracy percentage and time consumption, making it the most effective model for classifying hyperspectral imaging datasets. Hyperspectral imaging (dpeaa)DE-He213 K-Nearest neighbour support vector machine (SVM) (dpeaa)DE-He213 Convolutional neural network (CNN) (dpeaa)DE-He213 K-Nearest neighbour algorithm (KNN) (dpeaa)DE-He213 Principal component analysis (PCA) (dpeaa)DE-He213 Minimum noise fraction (MNF) (dpeaa)DE-He213 Chakravarty, Sujata aut Giri, Nimay Chandra aut Agyekum, Ephraim Bonah aut AboRas, Kareem M. (orcid)0000-0003-0485-468X aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 17(2024), 1 vom: 29. Jan. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:17 year:2024 number:1 day:29 month:01 https://dx.doi.org/10.1007/s44196-023-00370-y kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 29 01
spelling	10.1007/s44196-023-00370-y doi (DE-627)SPR054573068 (SPR)s44196-023-00370-y-e DE-627 ger DE-627 rakwb eng Dash, Satyabrata verfasserin aut Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract In recent years, deep learning techniques have presented a major role in hyperspectral image (HSI) classification. Most commonly Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has greatly advanced the accuracy of hyperspectral image classification, making it powerful tool for remote sensing applications. Deep structure learning, which involves multiple layers of neural network, has shown promising results in effectively addressing nonlinear problems and improving classification accuracy and reduce execution time. The exact categorization of ground topographies from hyperspectral data is a crucial and current research topic that has gotten a lot of attention. This research work focuses on hyperspectral image categorization utilizing several machine learning approaches such as support vector machine (SVM), K-Nearest Neighbour (KNN), CNN and LSTM. To reduce the number of superfluous and noisy bands in the dataset, Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) were utilized. Different performance evaluation measures like time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and Gmean have been taken to prove the efficacy of the models. Based on the simulation results, it is observed that the LSTM model outperforms the other models in terms of accuracy percentage and time consumption, making it the most effective model for classifying hyperspectral imaging datasets. Hyperspectral imaging (dpeaa)DE-He213 K-Nearest neighbour support vector machine (SVM) (dpeaa)DE-He213 Convolutional neural network (CNN) (dpeaa)DE-He213 K-Nearest neighbour algorithm (KNN) (dpeaa)DE-He213 Principal component analysis (PCA) (dpeaa)DE-He213 Minimum noise fraction (MNF) (dpeaa)DE-He213 Chakravarty, Sujata aut Giri, Nimay Chandra aut Agyekum, Ephraim Bonah aut AboRas, Kareem M. (orcid)0000-0003-0485-468X aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 17(2024), 1 vom: 29. Jan. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:17 year:2024 number:1 day:29 month:01 https://dx.doi.org/10.1007/s44196-023-00370-y kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 29 01
allfields_unstemmed	10.1007/s44196-023-00370-y doi (DE-627)SPR054573068 (SPR)s44196-023-00370-y-e DE-627 ger DE-627 rakwb eng Dash, Satyabrata verfasserin aut Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract In recent years, deep learning techniques have presented a major role in hyperspectral image (HSI) classification. Most commonly Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has greatly advanced the accuracy of hyperspectral image classification, making it powerful tool for remote sensing applications. Deep structure learning, which involves multiple layers of neural network, has shown promising results in effectively addressing nonlinear problems and improving classification accuracy and reduce execution time. The exact categorization of ground topographies from hyperspectral data is a crucial and current research topic that has gotten a lot of attention. This research work focuses on hyperspectral image categorization utilizing several machine learning approaches such as support vector machine (SVM), K-Nearest Neighbour (KNN), CNN and LSTM. To reduce the number of superfluous and noisy bands in the dataset, Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) were utilized. Different performance evaluation measures like time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and Gmean have been taken to prove the efficacy of the models. Based on the simulation results, it is observed that the LSTM model outperforms the other models in terms of accuracy percentage and time consumption, making it the most effective model for classifying hyperspectral imaging datasets. Hyperspectral imaging (dpeaa)DE-He213 K-Nearest neighbour support vector machine (SVM) (dpeaa)DE-He213 Convolutional neural network (CNN) (dpeaa)DE-He213 K-Nearest neighbour algorithm (KNN) (dpeaa)DE-He213 Principal component analysis (PCA) (dpeaa)DE-He213 Minimum noise fraction (MNF) (dpeaa)DE-He213 Chakravarty, Sujata aut Giri, Nimay Chandra aut Agyekum, Ephraim Bonah aut AboRas, Kareem M. (orcid)0000-0003-0485-468X aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 17(2024), 1 vom: 29. Jan. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:17 year:2024 number:1 day:29 month:01 https://dx.doi.org/10.1007/s44196-023-00370-y kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 29 01
allfieldsGer	10.1007/s44196-023-00370-y doi (DE-627)SPR054573068 (SPR)s44196-023-00370-y-e DE-627 ger DE-627 rakwb eng Dash, Satyabrata verfasserin aut Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract In recent years, deep learning techniques have presented a major role in hyperspectral image (HSI) classification. Most commonly Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has greatly advanced the accuracy of hyperspectral image classification, making it powerful tool for remote sensing applications. Deep structure learning, which involves multiple layers of neural network, has shown promising results in effectively addressing nonlinear problems and improving classification accuracy and reduce execution time. The exact categorization of ground topographies from hyperspectral data is a crucial and current research topic that has gotten a lot of attention. This research work focuses on hyperspectral image categorization utilizing several machine learning approaches such as support vector machine (SVM), K-Nearest Neighbour (KNN), CNN and LSTM. To reduce the number of superfluous and noisy bands in the dataset, Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) were utilized. Different performance evaluation measures like time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and Gmean have been taken to prove the efficacy of the models. Based on the simulation results, it is observed that the LSTM model outperforms the other models in terms of accuracy percentage and time consumption, making it the most effective model for classifying hyperspectral imaging datasets. Hyperspectral imaging (dpeaa)DE-He213 K-Nearest neighbour support vector machine (SVM) (dpeaa)DE-He213 Convolutional neural network (CNN) (dpeaa)DE-He213 K-Nearest neighbour algorithm (KNN) (dpeaa)DE-He213 Principal component analysis (PCA) (dpeaa)DE-He213 Minimum noise fraction (MNF) (dpeaa)DE-He213 Chakravarty, Sujata aut Giri, Nimay Chandra aut Agyekum, Ephraim Bonah aut AboRas, Kareem M. (orcid)0000-0003-0485-468X aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 17(2024), 1 vom: 29. Jan. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:17 year:2024 number:1 day:29 month:01 https://dx.doi.org/10.1007/s44196-023-00370-y kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 29 01
allfieldsSound	10.1007/s44196-023-00370-y doi (DE-627)SPR054573068 (SPR)s44196-023-00370-y-e DE-627 ger DE-627 rakwb eng Dash, Satyabrata verfasserin aut Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract In recent years, deep learning techniques have presented a major role in hyperspectral image (HSI) classification. Most commonly Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has greatly advanced the accuracy of hyperspectral image classification, making it powerful tool for remote sensing applications. Deep structure learning, which involves multiple layers of neural network, has shown promising results in effectively addressing nonlinear problems and improving classification accuracy and reduce execution time. The exact categorization of ground topographies from hyperspectral data is a crucial and current research topic that has gotten a lot of attention. This research work focuses on hyperspectral image categorization utilizing several machine learning approaches such as support vector machine (SVM), K-Nearest Neighbour (KNN), CNN and LSTM. To reduce the number of superfluous and noisy bands in the dataset, Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) were utilized. Different performance evaluation measures like time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and Gmean have been taken to prove the efficacy of the models. Based on the simulation results, it is observed that the LSTM model outperforms the other models in terms of accuracy percentage and time consumption, making it the most effective model for classifying hyperspectral imaging datasets. Hyperspectral imaging (dpeaa)DE-He213 K-Nearest neighbour support vector machine (SVM) (dpeaa)DE-He213 Convolutional neural network (CNN) (dpeaa)DE-He213 K-Nearest neighbour algorithm (KNN) (dpeaa)DE-He213 Principal component analysis (PCA) (dpeaa)DE-He213 Minimum noise fraction (MNF) (dpeaa)DE-He213 Chakravarty, Sujata aut Giri, Nimay Chandra aut Agyekum, Ephraim Bonah aut AboRas, Kareem M. (orcid)0000-0003-0485-468X aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 17(2024), 1 vom: 29. Jan. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:17 year:2024 number:1 day:29 month:01 https://dx.doi.org/10.1007/s44196-023-00370-y kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 1 29 01
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author	Dash, Satyabrata
spellingShingle	Dash, Satyabrata misc Hyperspectral imaging misc K-Nearest neighbour support vector machine (SVM) misc Convolutional neural network (CNN) misc K-Nearest neighbour algorithm (KNN) misc Principal component analysis (PCA) misc Minimum noise fraction (MNF) Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging
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topic_title	Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging Hyperspectral imaging (dpeaa)DE-He213 K-Nearest neighbour support vector machine (SVM) (dpeaa)DE-He213 Convolutional neural network (CNN) (dpeaa)DE-He213 K-Nearest neighbour algorithm (KNN) (dpeaa)DE-He213 Principal component analysis (PCA) (dpeaa)DE-He213 Minimum noise fraction (MNF) (dpeaa)DE-He213
topic	misc Hyperspectral imaging misc K-Nearest neighbour support vector machine (SVM) misc Convolutional neural network (CNN) misc K-Nearest neighbour algorithm (KNN) misc Principal component analysis (PCA) misc Minimum noise fraction (MNF)
topic_unstemmed	misc Hyperspectral imaging misc K-Nearest neighbour support vector machine (SVM) misc Convolutional neural network (CNN) misc K-Nearest neighbour algorithm (KNN) misc Principal component analysis (PCA) misc Minimum noise fraction (MNF)
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title	Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging
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title_full	Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging
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title_sort	minimum noise fraction and long short-term memory model for hyperspectral imaging
title_auth	Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging
abstract	Abstract In recent years, deep learning techniques have presented a major role in hyperspectral image (HSI) classification. Most commonly Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has greatly advanced the accuracy of hyperspectral image classification, making it powerful tool for remote sensing applications. Deep structure learning, which involves multiple layers of neural network, has shown promising results in effectively addressing nonlinear problems and improving classification accuracy and reduce execution time. The exact categorization of ground topographies from hyperspectral data is a crucial and current research topic that has gotten a lot of attention. This research work focuses on hyperspectral image categorization utilizing several machine learning approaches such as support vector machine (SVM), K-Nearest Neighbour (KNN), CNN and LSTM. To reduce the number of superfluous and noisy bands in the dataset, Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) were utilized. Different performance evaluation measures like time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and Gmean have been taken to prove the efficacy of the models. Based on the simulation results, it is observed that the LSTM model outperforms the other models in terms of accuracy percentage and time consumption, making it the most effective model for classifying hyperspectral imaging datasets. © The Author(s) 2023
abstractGer	Abstract In recent years, deep learning techniques have presented a major role in hyperspectral image (HSI) classification. Most commonly Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has greatly advanced the accuracy of hyperspectral image classification, making it powerful tool for remote sensing applications. Deep structure learning, which involves multiple layers of neural network, has shown promising results in effectively addressing nonlinear problems and improving classification accuracy and reduce execution time. The exact categorization of ground topographies from hyperspectral data is a crucial and current research topic that has gotten a lot of attention. This research work focuses on hyperspectral image categorization utilizing several machine learning approaches such as support vector machine (SVM), K-Nearest Neighbour (KNN), CNN and LSTM. To reduce the number of superfluous and noisy bands in the dataset, Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) were utilized. Different performance evaluation measures like time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and Gmean have been taken to prove the efficacy of the models. Based on the simulation results, it is observed that the LSTM model outperforms the other models in terms of accuracy percentage and time consumption, making it the most effective model for classifying hyperspectral imaging datasets. © The Author(s) 2023
abstract_unstemmed	Abstract In recent years, deep learning techniques have presented a major role in hyperspectral image (HSI) classification. Most commonly Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has greatly advanced the accuracy of hyperspectral image classification, making it powerful tool for remote sensing applications. Deep structure learning, which involves multiple layers of neural network, has shown promising results in effectively addressing nonlinear problems and improving classification accuracy and reduce execution time. The exact categorization of ground topographies from hyperspectral data is a crucial and current research topic that has gotten a lot of attention. This research work focuses on hyperspectral image categorization utilizing several machine learning approaches such as support vector machine (SVM), K-Nearest Neighbour (KNN), CNN and LSTM. To reduce the number of superfluous and noisy bands in the dataset, Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) were utilized. Different performance evaluation measures like time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and Gmean have been taken to prove the efficacy of the models. Based on the simulation results, it is observed that the LSTM model outperforms the other models in terms of accuracy percentage and time consumption, making it the most effective model for classifying hyperspectral imaging datasets. © The Author(s) 2023
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title_short	Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging
url	https://dx.doi.org/10.1007/s44196-023-00370-y
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author2	Chakravarty, Sujata Giri, Nimay Chandra Agyekum, Ephraim Bonah AboRas, Kareem M.
author2Str	Chakravarty, Sujata Giri, Nimay Chandra Agyekum, Ephraim Bonah AboRas, Kareem M.
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up_date	2024-07-04T02:14:12.815Z
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Different performance evaluation measures like time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and Gmean have been taken to prove the efficacy of the models. 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Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging

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