Self-Supervised Deep Multi-Level Representation Learning Fusion-Based Maximum Entropy Subspace Clustering for Hyperspectral Band Selection
As one of the most important techniques for hyperspectral image dimensionality reduction, band selection has received considerable attention, whereas self-representation subspace clustering-based band selection algorithms have received quite a lot of attention with good effect. However, many of them...
Main Authors: | Yulei Wang, Haipeng Ma, Yuchao Yang, Enyu Zhao, Meiping Song, Chunyan Yu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-01-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/16/2/224 |
Similar Items
-
Sparsity Regularized Deep Subspace Clustering for Multicriterion-Based Hyperspectral Band Selection
by: Samiran Das, et al.
Published: (2022-01-01) -
Graph adaptive semi-supervised discriminative subspace learning for EEG emotion recognition
by: Fengzhe Jin, et al.
Published: (2023-09-01) -
An Unsupervised Momentum Contrastive Learning Based Transformer Network for Hyperspectral Target Detection
by: Yulei Wang, et al.
Published: (2024-01-01) -
Unsupervised Hyperspectral Band Selection via Multimodal Evolutionary Algorithm and Subspace Decomposition
by: Yunpeng Wei, et al.
Published: (2023-02-01) -
Dynamic Ensemble Learning With Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification
by: Hongliang Lu, et al.
Published: (2022-01-01)