Results 21 to 30 of about 57,543 (231)
Hyperspectral absorption microscopy using photoacoustic remote sensing
Optics Express, 2021 An improved method of remote optical absorption spectroscopy and hyperspectral optical absorption imaging is described which takes advantage of the photoacoustic remote sensing detection architecture. A wide collection of photoacoustic excitation wavelengths ranging from 210 nm to 1550 nm was provided by a nanosecond tunable source allowing access to ...
Kevan Bell +3 more
openaire +3 more sources
IET Image Processing, 2022 Although hyperspectral data, especially spaceborne images, are rich in spectral information, their spatial resolution is usually low due to the limitation of sensor design and other factors.
Haoyang Yu +5 more
doaj +1 more source
Editorial for Special Issue “Advances in Hyperspectral Data Exploitation”
Remote Sensing, 2022 Hyperspectral imaging (HSI) has emerged as a promising, advanced technology in remote sensing and has demonstrated great potential in the exploitation of a wide variety of data.
Chein-I Chang +8 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Convolutional neural networks (CNN) have led to a successful breakthrough for hyperspectral image classification (HSIC). Due to the intrinsic spatial-spectral specificities of a hyperspectral cube, feature extraction with 3-D convolution operation is a ...
Chunyan Yu +4 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
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
core +6 more sources
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
core +4 more sources
IEEE Access, 2023 Since hyperspectral remote sensing images are three-dimensional data cubes with spatial and spectral information, with many wavebands and high inter-band correlation, the number of training samples required for classification is greatly increased.
Chaozhu Zhang +3 more
doaj +1 more source
Performance Evaluation of Cluster Validity Indices (CVIs) on Multi/Hyperspectral Remote Sensing Datasets [PDF]
, 2016 The number of clusters (i.e., the number of classes) for unsupervised classification has been recognized as an important part of remote sensing image clustering analysis.
Dale, Patricia +4 more
core +3 more sources
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
core +9 more sources