Extended Subspace Projection Upon Sample Augmentation Based on Global Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification
Band redundancy and limitation of labeled samples restrict the development of hyperspectral image classification (HSIC) greatly. To address the earlier issues, the classification models such as subspace-based support vector machines, which have gained a certain advance but mainly concentrate on the...
Main Authors: | Jiaochan Hu, Xueji Shen, Haoyang Yu, Xiaodi Shang, Qiandong Guo, Bing Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9521806/ |
Similar Items
-
Union of Class-Dependent Collaborative Representation Based on Maximum Margin Projection for Hyperspectral Imagery Classification
by: Haoyang Yu, et al.
Published: (2021-01-01) -
Hyperspectral Imagery Classification Based on Multiscale Superpixel-Level Constraint Representation
by: Haoyang Yu, et al.
Published: (2020-10-01) -
Group Sparse Representation Based on Nonlocal Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification
by: Haoyang Yu, et al.
Published: (2018-05-01) -
Class Probability Propagation of Supervised Information Based on Sparse Subspace Clustering for Hyperspectral Images
by: Qing Yan, et al.
Published: (2017-09-01) -
Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image
by: Jinhuan Xu, et al.
Published: (2021-04-01)