A novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper.
Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs).
Apr 17, 2021 · Deep learning models have been widely applied to extract the high-level features of hyperspectral images (HSIs) due to their strong data mining ...
Many machine learning methods have been implemented for hyperspectral dimensionality reduction, such as Principal Component Analysis (PCA) (Wang et al. 2022a, b ...
A multi-level feature extraction network (MLFEN) for HSI classification is proposed. By combining the capability of CNN's local spatial–spectral feature ...
A novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper.
We propose an unsupervised approach, dual feature extraction network (DFEN) for HAD, to gradually build up ever-greater discrimination between the original ...
After fusing the features of HSI and LiDAR with semantic understanding, the unsupervised extraction of spectral-spatial-elevation fusion features is achieved.
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What is multiscale feature extraction?
Which neural networks for hyperspectral classification?
We have proposed a method that uses unsupervised band selection called optimal neighboring reconstruction (ONR), which extracts a subset of spectral bands.
Jan 27, 2022 · How to effectively overcome the above problems, extract multi-level nonlinear discriminant features from HSI data, and improve the ...