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A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification
2021
International Journal of Applied Earth Observation and Geoinformation
In comparison with conventional machine learning algorithms, deep learning can effectively express the deep features of remote sensing images. Considering the rich spectral and spatial information contained in hyperspectral images (HSIs), a combination method was proposed for HSI classification based on stacked autoencoder (SAE) and 3D deep residual network (3DDRN). Specifically, a SAE neural network was first built to reduce the dimensions of original HSIs. A 3D convolutional neural network
doi:10.1016/j.jag.2021.102459
fatcat:aejpjmdfqvfg5e3pqizclm4chu