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 …
Fast spectral clustering for unsupervised hyperspectral image classification
Hyperspectral image classification is a challenging and significant domain in the field of
remote sensing with numerous applications in agriculture, environmental science …
remote sensing with numerous applications in agriculture, environmental science …
Adaptive polygon generation algorithm for automatic building extraction
Y Zhu, B Huang, J Gao, E Huang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Buildings serve as the main places of human activities, and it is essential to automatically
extract each building instance for a wide range of applications. Recently, automatic building …
extract each building instance for a wide range of applications. Recently, automatic building …
Self-supervised deep subspace clustering for hyperspectral images with adaptive self-expressive coefficient matrix initialization
Deep subspace clustering network has shown its effectiveness in hyperspectral image (HSI)
clustering. However, there are two major challenges that need to be addressed: 1) lack of …
clustering. However, there are two major challenges that need to be addressed: 1) lack of …
SHCNet: A semi-supervised hypergraph convolutional networks based on relevant feature selection for hyperspectral image classification
Hyperspectral imagery classification is a challenging task due to the large number of
spectral bands, and low number of labeled samples. To overcome these issues, we propose …
spectral bands, and low number of labeled samples. To overcome these issues, we propose …
A dual Laplacian framework with effective graph learning for unified fair spectral clustering
X Zhang, Q Wang - Neurocomputing, 2024 - Elsevier
We consider the problem of spectral clustering under group fairness constraints, where
samples from each sensitive group are approximately proportionally represented in each …
samples from each sensitive group are approximately proportionally represented in each …
An improved unsupervised representation learning generative adversarial network for remote sensing image scene classification
Y Wei, X Luo, L Hu, Y Peng, J Feng - Remote Sensing Letters, 2020 - Taylor & Francis
Unsupervised representation learning plays an important role in remote sensing image
applications. Generative adversarial network (GAN) is the most popular unsupervised …
applications. Generative adversarial network (GAN) is the most popular unsupervised …
Sparsity-based clustering for large hyperspectral remote sensing images
Hyperspectral image (HSI) clustering is extremely challenging because of the complexity of
the image structure. Recently, the subspace clustering algorithms have achieved …
the image structure. Recently, the subspace clustering algorithms have achieved …
JGSED: An end-to-end spectral clustering model for joint graph construction, spectral embedding and discretization
Most of the existing graph-based clustering models performed clustering by adopting a two-
stage strategy which first completes the spectral embedding from a given fixed graph and …
stage strategy which first completes the spectral embedding from a given fixed graph and …