Research output: Contribution to journal › Article › peer-review
Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging
AU - Dash, Satyabrata
AU - Chakravarty, Sujata
AU - Giri, Nimay
AU - Agyekum, Ephraim
AU - Aboras, Kareem
N1 - The authors gratefully acknowledge the Management of GITAM Deemed to be University, Andhra Pradesh, India for providing the facilities to carry out the research work.
PY - 2024
Y1 - 2024
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85183416510
UR - https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=tsmetrics&SrcApp=tsm_test&DestApp=WOS_CPL&DestLinkType=FullRecord&KeyUT=001151071300002
U2 - 10.1007/s44196-023-00370-y
DO - 10.1007/s44196-023-00370-y
M3 - Article
VL - 17
JO - International Journal of Computational Intelligence Systems
JF - International Journal of Computational Intelligence Systems
SN - 1875-6883
IS - 1
M1 - 16
ER -
ID: 52293865