Model-based co-clustering for hyperspectral images
A model-based co-clustering algorithm for hyperspectral images is presented. This algorithm, which relies on the probabilistic latent block model for continuous data, aims to cluster both the pixels and the spectral features of the images. This approach has been applied to a benchmark Raman imaging...
Main Authors: | Julien Jacques, Cyril Ruckebusch |
---|---|
Format: | Article |
Language: | English |
Published: |
IM Publications Open
2016-10-01
|
Series: | Journal of Spectral Imaging |
Subjects: | |
Online Access: | https://www.impublications.com/download.php?code=I05_a3 |
Similar Items
-
Biodiversity Assessment Using Hierarchical Agglomerative Clustering and Spectral Unmixing over Hyperspectral Images
by: J. Danilo Chinea, et al.
Published: (2013-10-01) -
Deep Spatial-Spectral Subspace Clustering for Hyperspectral Images Based on Contrastive Learning
by: Xiang Hu, et al.
Published: (2021-11-01) -
From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images
by: Shaoguang Huang, et al.
Published: (2023-05-01) -
Weighted fuzzy clustering for (fuzzy) constraints in multivariate image analysis–alternating least square of hyperspectral images
by: Siewert Hugelier, et al.
Published: (2016-12-01) -
Spatial–Spectral Constrained Adaptive Graph for Hyperspectral Image Clustering
by: Xing-Hui Zhu, et al.
Published: (2022-08-01)