Results 11 to 20 of about 6,322 (191)
Multispectral Laser-Scanning Photoacoustic Microscopy With SRS-Generated Wavelengths for Skin Chromophore Characterization. [PDF]
J BiophotonicsUnmixed maps showing vascular oxygen saturation (sO2), relative collagen, and relative melanin distributions. Maps were extracted from multispectral imaging of B6 and SKH1 Nude mouse abdominal skin using the proposed MS‐LS‐PAM system. ABSTRACT We present a single pump‐source, multispectral laser‐scanning photoacoustic microscopy (MS‐LS‐PAM) platform ...
Khansari A +6 more
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A Hybrid Diffusion Model Enhances Multiparametric 3D Photoacoustic Computed Tomography. [PDF]
Adv Sci (Weinh)The hybrid diffusion PACT (HD‐PACT) system enhances dynamic multiparametric information using only 128–256 ultrasound elements. By overcoming limited‐view artifacts, HD‐PACT refines structural and functional information such as oxygen saturation observed in 3D premium PACT with > 1000 elements. Through cost‐effective and faster multiparametric imaging,
Jeong H +5 more
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Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection
Remote Sensing, 2021 Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal.
Genping Zhao +4 more
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Double Regression‐Based Sparse Unmixing for Hyperspectral Images [PDF]
Journal of Sensors, 2021 Sparse unmixing has attracted widespread attention from researchers, and many effective unmixing algorithms have been proposed in recent years. However, most algorithms improve the unmixing accuracy at the cost of large calculations. Higher unmixing accuracy often leads to higher computational complexity.
Shuaiyang Zhang +5 more
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Hyperspectral Sparse Unmixing With Spectral-Spatial Low-Rank Constraint
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Spectral unmixing is a consequential preprocessing task in hyperspectral image interpretation. With the help of large spectral libraries, unmixing is equivalent to finding the optimal subset of the library entries that can best model the image.
Fan Li +5 more
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Approximate Sparse Regularized Hyperspectral Unmixing [PDF]
Mathematical Problems in Engineering, 2014 Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing ...
Chengzhi Deng +6 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 With the support of spectral libraries, sparse unmixing techniques have gradually developed. However, some existing sparse unmixing algorithms suffer from problems, such as insufficient utilization of spatial information and sensitivity to noise.
Yao Liang +4 more
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Hyperspectral Unmixing Based on Spectral and Sparse Deep Convolutional Neural Networks
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral unmixing refers to the process of obtaining endmembers and abundance vectors through linear or nonlinear models. The traditional linear unmixing model assumes that each mixed pixel can be represented by a linear combination of endmembers ...
Lulu Wan +3 more
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Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery
Remote Sensing, 2017 Spatial regularization based sparse unmixing has attracted much attention in the hyperspectral remote sensing image processing field, which combines spatial information consideration with a sparse unmixing model, and has achieved improved fractional ...
Ruyi Feng +3 more
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Collaborative Sparse Regression for Hyperspectral Unmixing [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2014 Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer).
Marian-Daniel Iordache +2 more
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