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Intrinsic dimensionality estimation and dimensionality reduction through scale space filtering
2009
2009 16th International Conference on Digital Signal Processing
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Dimensionality reduction techniques are designed to exploit the fact that most high-dimensional datasets from the real world do not uniformly fill the hyperspaces in which they are represented but instead their distributions ...
Experimental results on real hyperspectral datasets demonstrate that appropriate vector-valued scale space filtering significantly contributes to the intrinsic dimension estimation and dimensionality reduction ...
One would expect that, as the number of hyperspectral bands increases, the accuracy of classification should, also, increase. ...
doi:10.1109/icdsp.2009.5201196
fatcat:7ksgzjaq3bhrpo7he7c2io3pxm
Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep
[article]
2020
arXiv
pre-print
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extraction and its application on hyperspectral image classification. ...
accurate analysis of hyperspectral images. ...
Melba Crawford for providing the Indian Pines 2010 Data and the National Center for Airborne Laser Mapping (NCALM), the University of Houston, and the IEEE GRSS Fusion Committee for providing the Houston ...
arXiv:2003.02822v2
fatcat:2l37q46y6ndqjooo6pkcqezmzi
Hyperspectral Anomaly Detection based on Machine Learning: An Overview
2022
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Hyperspectral anomaly detection (HAD) is an important hyperspectral image application. ...
While most of the existed researches are related to statistic-based and distance-based techniques, by summarizing the background samples with certain models, and then, finding the very few outliers by ...
Subsequently, the latent representations are learned by an AE network with the embedding manifold constraints to preserve the intrinsic structure of hyperspectral data. ...
doi:10.1109/jstars.2022.3167830
fatcat:zdhdwbglrnbjfdf5w5trsopizi
The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch
2010
Advances in Astronomy
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Here we offer brief overviews of a number of concepts, techniques and developments that are vital to the analysis and visualization of complex datasets and images. ...
the problems presented by the new data sets have been addressed by other disciplines such as applied mathematics, statistics and machine learning and have been utilized by other sciences such as space-based ...
They would also like to thank the referee for constructive suggestions that led to substantial improvements of the article. The first author would like to thank Michael Werner for support. ...
doi:10.1155/2010/350891
fatcat:vrbbl3xdnvhedacuon5snse4fi
2021 Index IEEE Signal Processing Letters Vol. 28
2021
IEEE Signal Processing Letters
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, LSP 2021 1908-1912 Progressive Class-Based Expansion Learning for Image Classification. ...
., +, LSP 2021 728-732 Multi-Dimensional Edge Features Graph Neural Network on Few-Shot Image Classification. ...
doi:10.1109/lsp.2022.3145253
fatcat:a3xqvok75vgepcckwnhh2mty74
Intrinsic Dimension Estimation: Relevant Techniques and a Benchmark Framework
2015
Mathematical Problems in Engineering
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However, the problem is still open since most techniques cannot efficiently deal with datasets drawn from manifolds of high intrinsic dimension and nonlinearly embedded in higher dimensional spaces. ...
In the field of geophysical signal processing, hyperspectral images, whose pixels represent spectra generated by the combination of an unknown set of independent contributions, called endmembers, often ...
(or locally smooth) manifold structure, eventually embedded in a higher dimensional space through a nonlinear smooth mapping; in this case, the id to be estimated is the manifold's topological dimension ...
doi:10.1155/2015/759567
fatcat:7jk7jihtgzaczlrrxr4isfad2a
Table of Contents
2021
IEEE Signal Processing Letters
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Al-Shabi Progressive Class-Based Expansion Learning for Image Classification . . . . . . . . . . . . . . . . . . . H. Wang, H. Zhao, and X. ...
Mukherjee Multi-Dimensional Edge Features Graph Neural Network on Few-Shot Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/lsp.2021.3134551
fatcat:ab4b4tb5rrcu5cq6aifdekrizq
2021 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 32
2021
IEEE Transactions on Neural Networks and Learning Systems
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TNNLS May 2021 2157-2168 Hypersphere-Based Weight Imprinting for Few-Shot Learning on Embedded Devices. ...
., +, TNNLS Nov. 2021 5008-5021 Naive Gabor Networks for Hyperspectral Image Classification. ...
doi:10.1109/tnnls.2021.3134132
fatcat:2e7comcq2fhrziselptjubwjme
2021 Index IEEE Transactions on Image Processing Vol. 30
2021
IEEE Transactions on Image Processing
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., +, TIP 2021 39-54 Hierarchical and Interactive Refinement Network for Edge-Preserving Salient Object Detection. ...
., +, TIP 2021 572-587 A Supervised Segmentation Network for Hyperspectral Image Classification. ...
doi:10.1109/tip.2022.3142569
fatcat:z26yhwuecbgrnb2czhwjlf73qu
Table of Contents
2021
IEEE Signal Processing Letters
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Sun Progressive Class-Based Expansion Learning for Image Classification . . . . . . . . . . . . . . . . . . . H. Wang, H. Zhao, and X. ...
Clifford Multi-Dimensional Edge Features Graph Neural Network on Few-Shot Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/lsp.2021.3134549
fatcat:m6obtl7k7zdqvd62eo3c4tptfy
Automatic Framework for Spectral–Spatial Classification Based on Supervised Feature Extraction and Morphological Attribute Profiles
2014
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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However, most of the existing classification techniques have been developed for the analysis of multispectral images, and consequently, they are not usually efficient for the classification of hyperspectral ...
Beside the importance of classification accuracies, another critical issue for the purpose of hyperspectral image classification is simplicity and speed of the applied approaches. ...
Gamba from the University of Pavia, Italy, for providing the ROSIS data and corresponding reference information and Dr. P. Marpu for his contributions. ...
doi:10.1109/jstars.2014.2298876
fatcat:nxj4xswdlvc3bb47wujib2lahi
Hyperspectral Image Classification – Traditional to Deep Models: A Survey for Future Prospects
[article]
2021
arXiv
pre-print
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Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. ...
This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. ...
ACKNOWLEDGMENT The authors thanks to Ganesan Narayanasamy who is leading IBM OpenPOWER/POWER enablement and ecosystem worldwide for his support to get the IBM AC922 system's access. ...
arXiv:2101.06116v2
fatcat:2duwvojkybgufo4kf6sbc6hdva
Hyperspectral Image Classification - Traditional to Deep Models: A Survey for Future Prospects
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. ...
This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. ...
For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited. ...
doi:10.1109/jstars.2021.3133021
fatcat:pfjpzilfjfgbpa7kvekec2pixi
A Review of Kernel Methods in Remote Sensing Data Analysis
[chapter]
2011
Optical Remote Sensing
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Kernel methods have proven effective in the analysis of images of the Earth acquired by airborne and satellite sensors. ...
These properties are particularly appropriate for remote sensing data analysis. ...
A SVM method for objectoriented classification was proposed in [81] , while maximum likelihood classifiers for pixel-based classification was presented in [82] . ...
doi:10.1007/978-3-642-14212-3_10
fatcat:bq2ttaaxdndjlpjaxl57z6eh6e
Computationally Efficient Learning of Statistical Manifolds
[article]
2022
arXiv
pre-print
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The novelty of our approximation is the strongly consistent distance estimators based on independent and identically distributed samples from probability distributions. ...
By exploiting the connection between Hellinger/total variation distance for discrete distributions and the L2/L1 norm, we demonstrate that the proposed distance estimators, combined with approximate nearest ...
We would also like to acknowledge the support of the Australian Research Council (ARC) Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) for this project. ...
arXiv:2103.11773v2
fatcat:xcmem2vwcrgongpke4gd4gsdfa
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