Abstract
Present paper highlights the multispectral high-resolution satellite image has been classified using hybrid convolution neural network. Till now, improving the accuracy of image classification is one of the most significant research area in the domain of remote sensing. The most common deep learning technique such as convolution neural network (CNN) is used for image classification which becomes newer. Extraction of spatial and spectral information from satellite image using the 3D CNN approach is much more complex. While 2D CNN method is mainly used for extraction of spatial information, both spatial and spectral information are available in the multispectral satellite images. In this article, two CNN models (3D and 2D CNN) are integrated into hybrid CNN model which has been applied to multispectral satellite image for extraction of more precise land cover information. The classification results are validated using the overall accuracy and the Kappa statistic which was obtained through compared with the classified data and the Google Earth observation data. Hybrid CNN approach outcome is distinguished with the other methods such as fuzzy C-means (FCM), maximum likelihood classifier (MLC), and self-organizing maps (SOM). The classification accuracy for hybrid CNN model was found 95.17%, which is much higher than the other techniques.
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Kundu, K., Halder, P., Mandal, J.K. (2022). Multispectral Satellite Image Classification Using Hybrid Convolution Neural Network. In: Mandal, J.K., Buyya, R., De, D. (eds) Proceedings of International Conference on Advanced Computing Applications. Advances in Intelligent Systems and Computing, vol 1406. Springer, Singapore. https://doi.org/10.1007/978-981-16-5207-3_45
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DOI: https://doi.org/10.1007/978-981-16-5207-3_45
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