Unsupervised Feature Extraction for Reliable Hyperspectral Imagery Clustering via Dual Adaptive Graphs

Unsupervised Feature Extraction for Reliable Hyperspectral Imagery Clustering via Dual Adaptive Graphs

Hyperspectral imagery (HSI) clustering aims to assign pixel-wise data with large amount of spectral bands into different groups, where each group indicates one of land-cover objects existed in HSI. Without available label information in clustering task, the clustering performance heavily depends on...

Full description

Bibliographic Details
Main Authors: Jinyong Chen, Qidi Wu, Kang Sun
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Hyperspectral imagery
reliable clustering
unsupervised feature extraction
adaptive graph learning
low-rank representation
Online Access:https://ieeexplore.ieee.org/document/9395579/

Internet

https://ieeexplore.ieee.org/document/9395579/

Similar Items