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http://elar.urfu.ru/handle/10995/141472
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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2-s2.0-85183416510.pdf | 3,54 MB | Adobe PDF | Просмотреть/Открыть |
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