Multi-view learning for hyperspectral image classification: An overview
Hyperspectral images (HSI) are obtained from hyperspectral imaging sensors to capture the
object's information in hundreds of spectral bands. However, how to make full advantage of …
object's information in hundreds of spectral bands. However, how to make full advantage of …
Contrastive multi-view subspace clustering of hyperspectral images based on graph convolutional networks
High-dimensional and complex spectral structures make the clustering of hyperspectral
images (HSIs) a challenging task. Subspace clustering is an effective approach for …
images (HSIs) a challenging task. Subspace clustering is an effective approach for …
Hyperspectral image clustering: Current achievements and future lines
Hyperspectral remote sensing organically combines traditional space imaging with
advanced spectral measurement technologies, delivering advantages stemming from …
advanced spectral measurement technologies, delivering advantages stemming from …
From model-based optimization algorithms to deep learning models for clustering hyperspectral images
Hyperspectral images (HSIs), captured by different Earth observation airborne and space-
borne systems, provide rich spectral information in hundreds of bands, enabling far better …
borne systems, provide rich spectral information in hundreds of bands, enabling far better …
EMVCC: Enhanced multi-view contrastive clustering for hyperspectral images
Cross-view consensus representation plays a critical role in hyperspectral images (HSIs)
clustering. Recent multi-view contrastive cluster methods utilize contrastive loss to extract …
clustering. Recent multi-view contrastive cluster methods utilize contrastive loss to extract …
Hybrid-hypergraph regularized multiview subspace clustering for hyperspectral images
Clustering algorithms play an essential and unique role in classification tasks, especially
when annotated data are unavailable or very scarce. Current clustering approaches in …
when annotated data are unavailable or very scarce. Current clustering approaches in …
Deep spatial-spectral subspace clustering for hyperspectral images based on contrastive learning
X Hu, T Li, T Zhou, Y Peng - Remote Sensing, 2021 - mdpi.com
Hyperspectral image (HSI) clustering is a major challenge due to the redundant spectral
information in HSIs. In this paper, we propose a novel deep subspace clustering method that …
information in HSIs. In this paper, we propose a novel deep subspace clustering method that …
Bipartite Graph-based Projected Clustering with Local Region Guidance for Hyperspectral Imagery
Hyperspectral image (HSI) clustering is challenging to divide all pixels into different clusters
because of the absent labels, large spectral variability and complex spatial distribution …
because of the absent labels, large spectral variability and complex spatial distribution …
Affinity propagation based on structural similarity index and local outlier factor for hyperspectral image clustering
H Ge, L Wang, H Pan, Y Zhu, X Zhao, M Liu - Remote Sensing, 2022 - mdpi.com
In hyperspectral remote sensing, the clustering technique is an important issue of concern.
Affinity propagation is a widely used clustering algorithm. However, the complex structure of …
Affinity propagation is a widely used clustering algorithm. However, the complex structure of …
Dual Graph Learning Affinity Propagation for Multimodal Remote Sensing Image Clustering
Multimodal remote sensing image recognition aims to identify a category of land cover for
every pixel with consistency and complementary information provided by different …
every pixel with consistency and complementary information provided by different …