Discriminant analysis-based dimension reduction for hyperspectral image classification: A survey of the most recent advances and an experimental comparison of …
Hyperspectral imagery contains hundreds of contiguous bands with a wealth of spectral
signatures, making it possible to distinguish materials through subtle spectral discrepancies …
signatures, making it possible to distinguish materials through subtle spectral discrepancies …
Feature learning using spatial-spectral hypergraph discriminant analysis for hyperspectral image
Hyperspectral image (HSI) contains a large number of spatial-spectral information, which
will make the traditional classification methods face an enormous challenge to discriminate …
will make the traditional classification methods face an enormous challenge to discriminate …
Semisupervised feature extraction of hyperspectral image using nonlinear geodesic sparse hypergraphs
Y Duan, H Huang, T Wang - IEEE Transactions on Geoscience …, 2021 - ieeexplore.ieee.org
Recently, the sparse representation (SR)-based graph embedding method has been
extensively used in feature extraction (FE) tasks, but it is hard to reveal the complex manifold …
extensively used in feature extraction (FE) tasks, but it is hard to reveal the complex manifold …
Local geometric structure feature for dimensionality reduction of hyperspectral imagery
Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data
and separate the interclass data, and it is very useful to analyze the high-dimensional data …
and separate the interclass data, and it is very useful to analyze the high-dimensional data …
Unsupervised feature extraction in hyperspectral images based on Wasserstein generative adversarial network
Feature extraction (FE) is a crucial research area in hyperspectral image (HSI) processing.
Recently, due to the powerful ability of deep learning (DL) to extract spatial and spectral …
Recently, due to the powerful ability of deep learning (DL) to extract spatial and spectral …
Semisupervised sparse manifold discriminative analysis for feature extraction of hyperspectral images
F Luo, H Huang, Z Ma, J Liu - IEEE Transactions on Geoscience …, 2016 - ieeexplore.ieee.org
The graph embedding (GE) framework is very useful to extract the discriminative features of
hyperspectral images (HSIs) for classification. However, a major challenge of GE is how to …
hyperspectral images (HSIs) for classification. However, a major challenge of GE is how to …
Quantitative estimation of soil properties using hybrid features and RNN variants
Estimating soil properties is important for maximizing the production of crops in sustainable
agriculture. The hyperspectral data next input depends upon the previous one, and the …
agriculture. The hyperspectral data next input depends upon the previous one, and the …
Local constraint-based sparse manifold hypergraph learning for dimensionality reduction of hyperspectral image
Y Duan, H Huang, Y Tang - IEEE Transactions on Geoscience …, 2020 - ieeexplore.ieee.org
Sparse representation-based graph embedding methods have been successfully applied to
dimensionality reduction (DR) in recent years. However, these approaches usually become …
dimensionality reduction (DR) in recent years. However, these approaches usually become …
Modified tensor locality preserving projection for dimensionality reduction of hyperspectral images
By considering the cubic nature of hyperspectral image (HSI) to address the issue of the
curse of dimensionality, we have introduced a tensor locality preserving projection (TLPP) …
curse of dimensionality, we have introduced a tensor locality preserving projection (TLPP) …
Semi-supervised enhanced discriminative local constraint preserving projection for dimensionality reduction of medical hyperspectral images
H Gao, M Yang, X Cao, Q Liu, P Xu - Computers in Biology and Medicine, 2023 - Elsevier
Microscopic hyperspectral images has the advantage of containing rich spatial and spectral
information. However, the large number of spectral bands provides a significant amount of …
information. However, the large number of spectral bands provides a significant amount of …