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Wenbo Yu
Researcher at Harbin Institute of Technology
Publications - 18
Citations - 112
Wenbo Yu is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Hyperspectral imaging & Feature extraction. The author has an hindex of 3, co-authored 13 publications receiving 59 citations.
Papers
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Proceedings ArticleDOI
Convolutional neural network based classification for hyperspectral data
TL;DR: A novel deep learning classification method for hyperspectral data based on convolutional neural network is proposed, to restructure spectral feature images and choose convolution filters with a reasonable size so that the spectral features of different land coverings in high dimensions can be extracted properly.
Journal ArticleDOI
Spatial Revising Variational Autoencoder-Based Feature Extraction Method for Hyperspectral Images
Wenbo Yu,Miao Zhang,Yi Shen +2 more
TL;DR: A novel unsupervised hyperspectral feature extraction architecture based on spatial revising variational autoencoder (AE) achieves better classification results compared with comparison methods and the proposed loss function guarantees the consistency of the probability distributions of various latent spatial features, which obtained from the same neighbor region.
Journal ArticleDOI
Learning a local manifold representation based on improved neighborhood rough set and LLE for hyperspectral dimensionality reduction
Wenbo Yu,Miao Zhang,Yi Shen +2 more
TL;DR: Experimental results performed over two real-world hyperspectral datasets indicate the proposed INRSLLE not only considers the spectral-spatial information of hyperspectrals data, but also selects more suitable neighbors on local manifolds and increases the anti-noise ability.
Journal ArticleDOI
Convolutional Two-Stream Generative Adversarial Network-Based Hyperspectral Feature Extraction
TL;DR: A convolutional two-stream network based on the improved Wasserstein generative adversarial network (WGAN) is proposed for unsupervised hyperspectral spatial–spectral feature extraction and results show the feasibility and potential of this network.
Proceedings ArticleDOI
Mutual information and clone selection algorithm based hyperspectral band selection method
Wenbo Yu,Miao Zhang,Yi Shen +2 more
TL;DR: The proposed hyperspectral clone selection algorithm based band selection method (CSABS) chooses an improved multi-dimensional mutual information method as the measure criterion, can select bands with richer information and lower redundancy from hyperspectRAL data and it is effective due to low reconstruction error.