A new subspace clustering strategy for AI-based data analysis in IoT system
Z Cui, X Jing, P Zhao, W Zhang… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
The Internet-of-Things (IoT) technology is widely used in various fields. In the Earth
observation system, hyperspectral images (HSIs) are acquired by hyperspectral sensors and …
observation system, hyperspectral images (HSIs) are acquired by hyperspectral sensors and …
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 …
Deep spatial-spectral subspace clustering for hyperspectral image
Hyperspectral image (HSI) clustering is a challenging task due to the complex
characteristics in HSI data, such as spatial-spectral structure, high-dimension, and large …
characteristics in HSI data, such as spatial-spectral structure, high-dimension, and large …
Graph convolutional subspace clustering: A robust subspace clustering framework for hyperspectral image
Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI
data. Subspace clustering has been proven to be powerful for exploiting the intrinsic …
data. Subspace clustering has been proven to be powerful for exploiting the intrinsic …
Subspace clustering for hyperspectral images via dictionary learning with adaptive regularization
Sparse subspace clustering (SSC) has emerged as an effective approach for the automatic
analysis of hyperspectral images (HSI). Traditional SSC-based approaches employ the input …
analysis of hyperspectral images (HSI). Traditional SSC-based approaches employ the input …
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 …
Fast projected fuzzy clustering with anchor guidance for multimodal remote sensing imagery
Multimodal remote sensing image recognition is a popular research topic in the field of
remote sensing. This recognition task is mostly solved by supervised learning methods that …
remote sensing. This recognition task is mostly solved by supervised learning methods that …
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 …
Graph regularized residual subspace clustering network for hyperspectral image clustering
Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI
data. Subspace clustering methods have achieved promising clustering performance in HSI …
data. Subspace clustering methods have achieved promising clustering performance in HSI …
Fast spectral clustering with anchor graph for large hyperspectral images
The large-scale hyperspectral image (HSI) clustering problem has attracted significant
attention in the field of remote sensing. Most traditional graph-based clustering methods still …
attention in the field of remote sensing. Most traditional graph-based clustering methods still …