Results 21 to 30 of about 204,035 (336)
A low-cost hyperspectral scanner for natural imaging and the study of animal colour vision above and under water [PDF]
, 2019 Hyperspectral imaging is a widely used technology for industrial and scientific purposes, but the high cost and large size of commercial setups have made them impractical for most basic research.
Baden, T, Nevala, N E
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Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery [PDF]
, 2009 This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing ...
Alfred O. Hero +5 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Band redundancy and limitation of labeled samples restrict the development of hyperspectral image classification (HSIC) greatly. To address the earlier issues, the classification models such as subspace-based support vector machines, which have gained a ...
Jiaochan Hu +5 more
doaj +1 more source
High-resolution cathodoluminescence hyperspectral imaging of nitride nanostructures [PDF]
, 2012 Hyperspectral cathodoluminescence imaging provides spectrally and spatially resolved information on luminescent materials within a single dataset. Pushing the technique toward its ultimate nanoscale spatial limit, while at the same time spectrally ...
Chaowang Liu +9 more
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Implementation strategies for hyperspectral unmixing using Bayesian source separation [PDF]
, 2010 Bayesian Positive Source Separation (BPSS) is a useful unsupervised approach for hyperspectral data unmixing, where numerical non-negativity of spectra and abundances has to be ensured, such in remote sensing.
Dobigeon, Nicolas +5 more
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Band Subset Selection for Hyperspectral Image Classification
Remote Sensing, 2018 This paper develops a new approach to band subset selection (BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one ...
Chunyan Yu, Meiping Song, Chein-I Chang
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A New GPU Implementation of Support Vector Machines for Fast Hyperspectral Image Classification
Remote Sensing, 2020 The storage and processing of remotely sensed hyperspectral images (HSIs) is facing important challenges due to the computational requirements involved in the analysis of these images, characterized by continuous and narrow spectral channels.
Mercedes E. Paoletti +4 more
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Deep learning in remote sensing: a review [PDF]
, 2017 Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields ...
Fraundorfer, Friedrich +6 more
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Customizing kernel functions for SVM-based hyperspectral image classification [PDF]
, 2008 Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms.
Baofeng Guo +4 more
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Sketch-based subspace clustering of hyperspectral images [PDF]
, 2020 Sparse subspace clustering (SSC) techniques provide the state-of-the-art in clustering of hyperspectral images (HSIs). However, their computational complexity hinders their applicability to large-scale HSIs.
Du, Qian +3 more
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