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Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced multi-objective binary grey wolf Optimization

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Computers, Materials & Continua 2024年第6期79卷 3469-3493页

作者： Mehrdad Shoeibi Mohammad Mehdi Sharifi Nevisi Reza Salehi Diego Martín Zahra Halimi Sahba Baniasadi TheWPI Business School Worcester Polytechnic InstituteWorcesterMassachusetts01609-2280USA Department of Industrial Engineering Iran University of Science and TechnologyNarmakTehran16846-13114Iran ETSI de Telecomunicación Universidad Politécnica de MadridAv.Complutense 30Madrid28040Spain Department of Civil and Environmental Engineering University of MarylandCollege ParkMaryland20742USA Department of Industrial Engineering College of EngineeringUniversity of HoustonHoustonTexas77204USA

Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the *** band selection in HS images is crucial for improving the efficiency and accuracy of image *** process involves selecting the most informative spectral bands,which leads to a reduction in data *** on these key bands also enhances the accuracy of classification algorithms,as redundant or irrelevant bands,which can introduce noise and lower model performance,are *** this paper,we propose an approach for HS image classification using deep Q learning(DQL)and a novel multi-objective binary grey wolf optimizer(MOBGWO).We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image *** the suggested MOBGWO,a new sigmoid function is introduced as a transfer function to modify the wolves’*** primary objective of this classification is to reduce the number of bands while maximizing classification *** evaluate the effectiveness of our approach,we conducted experiments on publicly available HS image datasets,including Pavia University,Washington Mall,and Indian Pines *** compared the performance of our proposed method with several state-of-the-art deep learning(DL)and machine learning(ML)algorithms,including long short-term memory(LSTM),deep neural network(DNN),recurrent neural network(RNN),support vector machine(SVM),and random forest(RF).Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL ***-DQL shows greater accuracy in classifying most categories in both datasets *** the Indian Pine dataset,the MOBGWO-DQL architecture achieved a kappa coefficient(KC)of 97.68%and an overall accuracy(OA)of 94.32%.This was accompanied by the lowest root mean square error(RMSE)of 0.94,indicating very precise

关键词： Hyperspectral image classification reinforcement learning multi-objective binary grey wolf optimizer band selection

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