Abstract
In response to the problems of low accuracy and long time consumption in traditional hyperspectral remote sensing image classification methods, this paper proposes a hyperspectral remote sensing image classification method based on a three-dimensional convolutional neural network model. Firstly, the image data is preprocessed and normalized. Based on this, a three-dimensional convolutional neural network is introduced into the learning of image data. On this basis, by optimizing the overall connectivity parameters of the convolutional kernel function, hyperspectral remote sensing image classification based on the convolutional kernel function was achieved. Experiments have shown that the algorithm proposed in this article can accurately classify hyperspectral images and achieve good results.
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
Sadovnychiy, S.: Gabor features extraction and land-cover classification of urban hyperspectral images for remote sensing applications. Remote Sensing 13(15), 2914 (2021)
Jijon-Palma, M.E., Kern, J., Amisse, C., et al.: Improving stacked-autoencoders with 1D convolutional-nets for hyperspectral image land-cover classification. J. Appl. Remote. Sens. 2, 15 (2021)
Cao, H., Han, X., Li, J., et al.: Hyperspectral remote sensing image classification based on EMP and SVM with composite kernel. Geospatial Inf. 19(11), 14–18 (2021)
Liu, J., Ban, W., Chen, Y., et al.: Multi-dimensional CNN fused algorithm for hyperspectral remote sensing image classification. Chinese Journal of Lasers 48(16), 153–163 (2021)
Peng, Y., Wang, X., Zhang, J., et al.: Pre‐training of gated convolution neural network for remote sensing image super‐resolution. IET Image Processing 15(5), 11791188 (2021)
Anand, R., Veni, S., Aravinth, J.: Robust classification technique for hyperspectral images based on 3D-discrete wavelet transform. Remote Sensing 13(7), 1255 (2021)
Gong, H., Li, Q., Li, C., et al.: Multiscale information fusion for hyperspectral image classification based on hybrid 2D–3D CNN. Remote Sensing 13(12), 2268 (2021)
Xi, J., Ersoy, O.K., Fang, J., et al.: Wide sliding window and subsampling network for hyperspectral image classification. Remote Sensing 13(7), 1290 (2021)
Liu, X., Chen, S., Song, L., et al.: Self-attention negative feedback network for real-time image super-resolution. J. King Saud University - Computer and Information Sci. 34(8B), 6179–6186 (2022)
Liu, M., Tan, J., Tian, Y.: Decoding auditory attentional states by a 3D convolutional neural network model. Int. J. Psychophysiol. 168, 134–135 (2021)
Alameddine, J., Chehdi, K., Cariou, C.: Hierarchical unsupervised partitioning of large size data and its application to hyperspectral images. Remote Sensing 13(23), 4874 (2021)
Hawkesford, M.J.: A neural network method for classification of sunlit and shaded components of wheat canopies in the field using high-resolution hyperspectral imagery. Remote Sensing 13(5), 898 (2021)
Deshmukh, R.R., Ghule, A.: Wavelength selection and classification of hyperspectral non-imagery data to discriminate healthy and unhealthy vegetable leaves. Curr. Sci. 120(5), 932–936 (2021)
Macfarlane, F., Murray, P., Marshall, S., et al.: Investigating the effects of a combined spatial and spectral dimensionality reduction approach for aerial hyperspectral target detection applications. Remote Sensing 13(9), 1647 (2021)
Rossello, J.C., Graham, P., Prakash, A., et al.: Airborne hyperspectral data acquisition and processing in the arctic: a pilot study using the hyspex imaging spectrometer for wetland mapping. Remote Sensing 13(6), 1178 (2021)
Zhang, Y., Du, J., Pi, W., et al.: Deep learning classification of grassland desertification in china via low-altitude UAV hyperspectral remote sensing. Spectroscopy 37(1), 28+3035 (2022)
Ping, W., Fu, J., Qiao, W., et al.: Decision support system for hyperspectral remote-sensing data of Yellow River Estuary, China. Scientific Programming 2121(9), 1–17 (2021)
Li, K., Xu, J., Zhao, T., et al.: A fuzzy spectral clustering algorithm for hyperspectral image classification. IET Image Processing 15(12), 2810–2817 (2021)
Li, J., Shen, H., Li, H., et al.: Radiometric quality improvement of hyperspectral remote sensing images: a technical tutorial on variational framework. J. Applied Remote Sensing 15(3), 1–33 (2021)
Aknowledgement
2020 Anhui Province University Excellent and Top notch Talent Cultivation (gxgnfx2020112)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhao, P., Yin, X., Chen, S. (2024). Classification of Hyperspectral Remote Sensing Images Based on Three-Dimensional Convolutional Neural Network Model. In: Yun, L., Han, J., Han, Y. (eds) Advanced Hybrid Information Processing. ADHIP 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 548. Springer, Cham. https://doi.org/10.1007/978-3-031-50546-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-50546-1_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50545-4
Online ISBN: 978-3-031-50546-1
eBook Packages: Computer ScienceComputer Science (R0)