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Classification of Land Cover Hyperspectral Images Using Deep Convolutional Neural Network

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 914)


In this research, we proposed a novel deep convolutional neural network (DCNN) for land cover hyperspectral image classification. The Indian pines dataset was used to train and test the performance of the proposed model. Data augmentation, up-sampling, and zero-padding techniques were used to enhance the quality and quantity of the dataset. The proposed model was trained on the enhanced dataset using a graphical processing unit (GPU) environment. The trained model was tested using a test dataset and produced an accuracy of 99.3%. The testing accuracy of the proposed DCNN model was superior to other state-of-the-art machine learning techniques such as logistic regression (LR), decision tree (DT), support vector machine (SVM), and multilayer perceptron (MLP).


  • Land cover images
  • Hyperspectral images
  • Deep convolutional neural network
  • Data augmentation
  • Data up-sampling

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  • DOI: 10.1007/978-981-19-2980-9_8
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The authors are grateful to Vel Tech Technology Business Incubator, Chennai.

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Correspondence to Saurav Kr. Gupta .

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Pandian, J.A., Gupta, S.K., Kumar, R., Hazra, S., Kanchanadevi, K. (2022). Classification of Land Cover Hyperspectral Images Using Deep Convolutional Neural Network. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Electrical Engineering, vol 914. Springer, Singapore.

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  • Print ISBN: 978-981-19-2979-3

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