Improved Convolutional Neural Networks for the Classification of the Hyperspectral Image
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Abstract
In recent times, convolutional neural network (CNN) provides improved performance on various image processing analysis. This includes classification of images even with redundant information over various imaging application. With such aim, in this paper, the hyperspectral images are classified using CNN in spectral domain. The CNN architecture includes five different layers enables the classification of data samples by the CNN classifiers and discards redundant information. The experimental results test the efficacy of the model, where the results show that the CNN obtains higher classification accuracy than other methods.
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