Abstract
Classification of hyperspectral images (HSI) can benefit from deep learning models with deep architecture in remote sensing. In this letter, a novel method based on Convolutional Neural Network (CNN) is proposed for the classification of hyperspectral images. Due to using more spatio-spectral features for the classification of hyperspectral images, the proposed method outperforms the existing state-of-the-art classification techniques. Our proposed method first reduces the dimension of hyperspectral images using Principle component analysis (PCA). The spatial and spectral features are then exploited by a fixed size convolutional filter to generate the combine spatio-spectral feature maps. Finally, these feature maps are fed into a Multi-Layer Perceptron (MLP) classifier that predicts the class of the pixel vector. To validate the effectiveness of our proposed method, computer simulations are conducted using three datasets namely Indian Pines, Salinas and Pavia University and comparisons with existing techniques are made.
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Acknowledgments
This work is sponsored by the National Natural Science Foundation of China under Grant No. 61373063 and 61373062; the project of Ministry of Industry and Information Technology of China (Grant No. E0310/1112/02-1).
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Iltaf, A., Ullah, M., Shen, J., Wu, Z., Liu, C., Ahmad, Z. (2018). Digging More in Neural World: An Efficient Approach for Hyperspectral Image Classification Using Convolutional Neural Network. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 849. Springer, Singapore. https://doi.org/10.1007/978-981-13-0896-3_12
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DOI: https://doi.org/10.1007/978-981-13-0896-3_12
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