Abstract
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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, C., Liu, B., Liu, L., Zhu, Y., Hou, J., Liu, P., Li, X.: A review of deep learning used in the hyperspectral image analysis for agriculture. Artif. Intell. Rev. 54, 5205–5253 (2021). https://doi.org/10.1007/s10462-021-10018-y
Kavitha, K., Arivazhagan, S., Kanaga Sangeetha, I.: Hyperspectral image classification using support vector machine in Ridgelet domain. Natl. Acad. Sci. Lett. 38, 475–478 (2015). https://doi.org/10.1007/s40009-015-0361-9
Liu, L., Li, C., Lei, Y., Yin, J., Zhao, J.: Feature extraction for hyperspectral remote sensing image using weighted PCA-ICA. Arab. J. Geosci. 10, 307 (2017). https://doi.org/10.1007/s12517-017-3090-1
Baumgardner, M.F., Biehl, L.L., Landgrebe, D.A.: 220 Band AVIRIS hyperspectral image data set, 12 June 1992 Indian Pine Test Site 3(2015). https://purr.purdue.edu/publications/1947/1. https://doi.org/10.4231/R7RX991C
Mei, S., Geng, Y., Hou, J., Du, Q.: Learning hyperspectral images from RGB images via a coarse-to-fine CNN. Sci. China Inf. Sci. 65 (2022). https://doi.org/10.1007/s11432-020-3102-9
Fu, H., Sun, G., Ren, J., Zhang, A., Jia, X.: Fusion of PCA and segmented-PCA domain multiscale 2-D-SSA for effective spectral-spatial feature extraction and data classification in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 60 (2022). https://doi.org/10.1109/TGRS.2020.3034656
Pande, S., Banerjee, B.: HyperLoopNet: Hyperspectral image classification using multiscale self-looping convolutional networks. ISPRS J. Photogramm. Remote Sens. 183, 422–438 (2022). https://doi.org/10.1016/j.isprsjprs.2021.11.021
Gao, T., Chandran, A.K.N., Paul, P., Walia, H., Yu, H.: Hyperseed: An end-to-end method to process hyperspectral images of seeds. Sensors 21 (2021). https://doi.org/10.3390/s21248184
Mehalli, Z., Zigh, E., Loukil, A., Ali Pacha, A.: Hyperspectral data preprocessing of the Northwestern Algeria Region BT—Networking. Intell. Syst. Secur. (2022).
Mukhopadhyay, M. et al.: Facial emotion recognition based on textural pattern and convolutional neural network. In: 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), pp. 1–6. https://doi.org/10.1109/GUCON50781.2021.9573860
Wang, C., Zhang, L., Wei, W., Zhang, Y.: Hyperspectral image classification with data augmentation and classifier fusion. IEEE Geosci. Remote Sens. Lett. 17, 1420–1424 (2020). https://doi.org/10.1109/LGRS.2019.2945848
Zeng, J., Hu, W., Huang, F.: Analysis of hyperspectral image classification technology and application based on convolutional neural networks. In: 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), pp. 409–414 (2021). https://doi.org/10.1109/CEI52496.2021.9574493
Bodapati, S., et al.: Comparison and analysis of RNN-LSTMs and CNNs for social reviews classification. In: Bansal, J.C., Fung, L.C.C., Simic, M., Ghosh, A. (eds.) Advances in Applications of Data-Driven Computing. Advances in Intelligent Systems and Computing, vol. 1319. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-6919-1_4
Kumar, V., Singh, R.S., Dua, Y.: Morphologically dilated convolutional neural network for hyperspectral image classification. Signal Process. Image Commun. 101, 116549 (2022). https://doi.org/10.1016/j.image.2021.116549
Acknowledgements
The authors are grateful to Vel Tech Technology Business Incubator, Chennai.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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. https://doi.org/10.1007/978-981-19-2980-9_8
Download citation
DOI: https://doi.org/10.1007/978-981-19-2980-9_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2979-3
Online ISBN: 978-981-19-2980-9
eBook Packages: Computer ScienceComputer Science (R0)