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Showing 1 to 20 of 50 for “"Unmixing"”.

  1. Vegetation Reflectance Estimation Through Optimization of a Spectral Unmixing Model

    … data at canopy scale. The traditional spectral unmixing model and a variation that includes a color-matching process were tested in this study as methods to obtain an isolated vegetation reflectance from a mixed signal. Both spectral unmixing models were evaluated by observing the differences …

    sask Repository record for Vegetation Reflectance Estimation Through Optimization of a Spectral Unmixing Model (opens in a new tab)

  2. Manifold learning based spectral unmixing of hyperspectral remote sensing data

    … are not properly represented in linear spectral unmixing models. Although direct nonlinear unmixing models provide capability to capture nonlinear phenomena, they are difficult to formulate and the results are not always generalizable. Manifold learning based spectral unmixing accommodates …

    purdue-thes Repository record for Manifold learning based spectral unmixing of hyperspectral remote sensing data (opens in a new tab)

  3. Unmixing algorithms and materials for radiation detection in harsh environments

    Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms

    uiuc Repository record for Unmixing algorithms and materials for radiation detection in harsh environments (opens in a new tab)

  4. Understanding Soil Carbon Signatures from Hyperspectral Reflectance Data using Spectral Unmixing

    … data collection efforts designed explicitly for unmixing applications could enhance the quality of the results and contribute to a more comprehensive understanding of SOC spectrum and abundance. These findings motivate further development and refinement of spectral mixing models, as well as …

    mit Repository record for Understanding Soil Carbon Signatures from Hyperspectral Reflectance Data using Spectral Unmixing (opens in a new tab)

  5. Collection of endmembers and their separability for spectral unmixing in rangeland applications

    lethbridge

  6. Latent Dirichlet Variational Autoencoder: a novel approach for hyperspectral image analysis and pixel unmixing exploring deep learning architectures

    … hyperspectral image analysis, focusing on pixel unmixing and classification tasks. Recognizing the challenges of high data dimensionality and limited labeled data availability, this research proposes innovative techniques to improve both the accuracy and efficiency of hyperspectral image …

    uoit Repository record for Latent Dirichlet Variational Autoencoder: a novel approach for hyperspectral image analysis and pixel unmixing exploring deep learning architectures (opens in a new tab)

  7. Computational microscopy for sample analysis

    … we have developed a novel nonlinear matrix unmixing algorithm to separate fluorescence spectra distorted by absorption effect. By extending the method to tensor form, we have also demonstrated the performance of a nonlinear fluorescence tensor unmixing algorithm on spectral fluorescence …

    mit Repository record for Computational microscopy for sample analysis (opens in a new tab)

  8. Map-guided hyperspectral image superpixel segmentation using semi-supervised partial membership latent Dirichlet allocation

    … solve this problem, we introduce a hyperspectral unmixing based superpixel segmentation that leverages map information. We call this approach map-guided semi-supervised PM-LDA superpixel segmentation. The approach uses auxilliary map information to guide segmentation. The approach also leverages …

    missouri Repository record for Map-guided hyperspectral image superpixel segmentation using semi-supervised partial membership latent Dirichlet allocation (opens in a new tab)

  9. Discriminating and mapping soil variability with hyperspectral reflectance data.

    … from the images through linear spectral unmixing and compared with the measured fractions. Soils were accurately identified and classified in both image types. However, not all soil spectra were isolated from mixed pixels equally or successfully to provide accurate abundance fractions: …

    adelaide Repository record for Discriminating and mapping soil variability with hyperspectral reflectance data. (opens in a new tab)

  10. Land Cover Quantification using Autoencoder based Unsupervised Deep Learning

    … land cover quantification through hyperspectral unmixing using an unsupervised autoencoder. Land cover identification and classification is instrumental in urban planning, environmental monitoring and land management. With the technological advancements in remote sensing, hyperspectral imagery …

    vt Repository record for Land Cover Quantification using Autoencoder based Unsupervised Deep Learning (opens in a new tab)

  11. Experimental study of the effect of H2O-CO2-NaCl fluid immiscibility on the reaction calcite + quartz + rutile = sphene + CO2 at 2 KBAR

    … of several points along the curve suggest unmixing of the fluid phase, as evidenced by coexisting CO₂ vapor-rich and aqueous, halite-bearing inclusions. Results from 450-520 °C are listed below.

    vt Repository record for Experimental study of the effect of H2O-CO2-NaCl fluid immiscibility on the reaction calcite + quartz + rutile = sphene + CO2 at 2 KBAR (opens in a new tab)

  12. Shedding light on the molecular interactomes of fast, complex biological processes using multispectral imaging with uncompromised spatiotemporal resolution

    … PhD project, I developed an iterative, spectral unmixing algorithm which can spectrally unmix noisy multispectral datasets such as those deriving from live-cell imaging experiments. This algorithm outperforms conventional spectral unmixing approaches for unmixing low-signal multispectral data …

    cambridge Repository record for Shedding light on the molecular interactomes of fast, complex biological processes using multispectral imaging with uncompromised spatiotemporal resolution (opens in a new tab)

  13. Clinical applications of quantitative photoacoustic imaging

    … phantoms, and pigmented mice. Linear unmixing and a machine learning approach were compared to estimate blood oxygenation (sO<sub>2</sub>). A consistent trend of increasing sO<sub>2</sub> with increasing skin pigmentation was observed in all three settings, suggesting that spectral …

    cambridge Repository record for Clinical applications of quantitative photoacoustic imaging (opens in a new tab)

  14. Spectrally Resolved Detector Arrays for Multiplexed Biomedical Fluorescence Imaging

    … in solution. We also demonstrated high spectral unmixing precision, signal linearity with dye concentration, at depth in tissue mimicking phantoms, and delineation of four fluorescent dyes in vivo. After the successful demonstration of multiplexed fluorescence imaging in a wide-field set-up, we …

    cambridge Repository record for Spectrally Resolved Detector Arrays for Multiplexed Biomedical Fluorescence Imaging (opens in a new tab)

  15. Single-cell NAD(H) levels predict clonal lymphocyte expansion dynamics

    … T- and B-cells, prior to the first division, unmixing proliferative heterogeneity. We believe this supports a broader paradigm in which complex signaling networks converge on metabolic pathways to control single-cell behavior.

    penn Repository record for Single-cell NAD(H) levels predict clonal lymphocyte expansion dynamics (opens in a new tab)

  16. Multiplexed nanoparticle-based immunoassays

    … multiplexed immunosorbent assays. Spectral unmixing was investigated using mixtures of fluorophores and cadmium selenide quantum dots. Mixtures of up to four dyes were separated quantitatively using least squares minimisation, with relative standard error ranging from 0.5 to 13 %. Silica …

    nott-trent Repository record for Multiplexed nanoparticle-based immunoassays (opens in a new tab)

  17. Advanced Techniques for Automatic Change Detection in Multitemporal Hyperspectral Images

    … 4) A novel automatic multitemporal spectral unmixing approach to detect multiple changes in hyperspectral images. A multitemporal spectral mixture model is proposed to analyse the spectral variations at sub-pixel level, thus investigating in details the spectral composition of change and …

    trento Repository record for Advanced Techniques for Automatic Change Detection in Multitemporal Hyperspectral Images (opens in a new tab)

  18. Clay mineralogy of sediments and source materials in the York River tributary basin

    … of the clay in units of structure. This physical unmixing provides an explanation of the increase in illite reflections in sediments in the lower part of the stream. Montmorillonite is more frequent in the lower part of the stream because of its greater mobility than the other minerals. Results of …

    vt Repository record for Clay mineralogy of sediments and source materials in the York River tributary basin (opens in a new tab)

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