The potential of machine learning for a more responsible sourcing of critical raw materials
The digitization and automation of the raw material sector is required to attain the targets set
by the Paris Agreements and support the sustainable development goals defined by the …
by the Paris Agreements and support the sustainable development goals defined by the …
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 …
Spatial-spectral graph contrastive clustering with hard sample mining for hyperspectral images
Hyperspectral image (HSI) clustering is a fundamental yet challenging task that groups
image pixels with similar features into distinct clusters. Among various approaches …
image pixels with similar features into distinct clusters. Among various approaches …
Superpixel contracted neighborhood contrastive subspace clustering network for hyperspectral images
Deep subspace clustering (DSC) has achieved remarkable performances in the
unsupervised classification of hyperspectral images. However, previous models based on …
unsupervised classification of hyperspectral images. However, previous models based on …
A locally optimized model for hyperspectral and multispectral images fusion
K Ren, W Sun, X Meng, G Yang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The maintenance of spectral variability between subclass objects and the relationship
between hyperspectral (HS) bands have been a fundamental but challenging problem for …
between hyperspectral (HS) bands have been a fundamental but challenging problem for …
Large-scale hyperspectral image clustering using contrastive learning
Clustering of hyperspectral images is a fundamental but challenging task. The recent
development of hyperspectral image clustering has evolved from shallow models to deep …
development of hyperspectral image clustering has evolved from shallow models to deep …
Unsupervised data fusion with deeper perspective: A novel multisensor deep clustering algorithm
The ever-growing developments in technology to capture different types of image data [eg,
hyperspectral imaging and light detection and ranging (LiDAR)-derived digital surface …
hyperspectral imaging and light detection and ranging (LiDAR)-derived digital surface …
Spectral-spatial superpixel anchor graph-based clustering for hyperspectral imagery
Hyperspectral image (HSI) clustering has attracted great attention in the field of remote
sensing. General anchor-based clustering methods often suffer from the problems of …
sensing. General anchor-based clustering methods often suffer from the problems of …
Deep mutual information subspace clustering network for hyperspectral images
Hyperspectral image (HSI) clustering has attracted a great deal of attention, owing to lower
cost and higher application prospects. Deep subspace clustering has been proved to be an …
cost and higher application prospects. Deep subspace clustering has been proved to be an …
Spatial-Spectral Adaptive Graph Convolutional Subspace Clustering for Hyperspectral Image
Y Liu, E Zhu, Q Wang, J Li, S Liu, Y Hu… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
Graph convolution subspace clustering has been widely used in the field of hyperspectral
image (HSI) unsupervised classification due to its ability to aggregate neighborhood …
image (HSI) unsupervised classification due to its ability to aggregate neighborhood …