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Название: Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging
Авторы: Dash, S.
Chakravarty, S.
Giri, N. C.
Agyekum, E. B.
AboRas, K. M.
Дата публикации: 2024
Издатель: Springer Science and Business Media B.V.
Библиографическое описание: Dash, S., Chakravarty, S., Giri, N., Agyekum, E., & Aboras, K. (2024). Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging. International Journal of Computational Intelligence Systems, 17(1), [16]. https://doi.org/10.1007/s44196-023-00370-y
Аннотация: 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. © 2023, The Author(s).
Ключевые слова: CONVOLUTIONAL NEURAL NETWORK (CNN)
HYPERSPECTRAL IMAGING
K-NEAREST NEIGHBOUR ALGORITHM (KNN)
K-NEAREST NEIGHBOUR SUPPORT VECTOR MACHINE (SVM)
MINIMUM NOISE FRACTION (MNF)
PRINCIPAL COMPONENT ANALYSIS (PCA)
BRAIN
CLASSIFICATION (OF INFORMATION)
CONVOLUTION
CONVOLUTIONAL NEURAL NETWORKS
HYPERSPECTRAL IMAGING
IMAGE CLASSIFICATION
LEARNING ALGORITHMS
LEARNING SYSTEMS
LONG SHORT-TERM MEMORY
MULTILAYER NEURAL NETWORKS
NEAREST NEIGHBOR SEARCH
NETWORK LAYERS
PRINCIPAL COMPONENT ANALYSIS
REMOTE SENSING
CONVOLUTIONAL NEURAL NETWORK
K-NEAR NEIGHBOR ALGORITHM
K-NEAR NEIGHBOR SUPPORT VECTOR MACHINE
MINIMUM NOISE FRACTION
NEAREST-NEIGHBOR ALGORITHMS
NEAREST-NEIGHBOUR
PRINCIPAL COMPONENT ANALYSE
PRINCIPAL-COMPONENT ANALYSIS
SUPPORT VECTORS MACHINE
SUPPORT VECTOR MACHINES
URI: http://elar.urfu.ru/handle/10995/141472
Условия доступа: info:eu-repo/semantics/openAccess
cc-by
Идентификатор SCOPUS: 85183416510
Идентификатор WOS: 001151071300002
Идентификатор PURE: 52293865
ISSN: 1875-6883
DOI: 10.1007/s44196-023-00370-y
Карточка проекта РНФ: Fundamental Research Funds of Shaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices, (AFMD-KFJJ-21203); Youth Innovation Team of Shaanxi Universities; National Natural Science Foundation of China, NSFC, (51761145024); National Natural Science Foundation of China, NSFC; Xi’an Jiaotong University, XJTU; 23-42-00116
Располагается в коллекциях:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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