Results 41 to 50 of about 1,373 (228)
Remote Sensing, 2023 Spectral unmixing is one of the prime topics in hyperspectral image analysis, as images often contain multiple sources of spectra. Spectral variability is one of the key factors affecting unmixing accuracy, since spectral signatures are affected by ...
Ying Cheng +3 more
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A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for
Qiqi Zhu +4 more
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Hyperspectral Unmixing with Gaussian Mixture Model and Low-Rank Representation
Remote Sensing, 2019 Gaussian mixture model (GMM) has been one of the most representative models for hyperspectral unmixing while considering endmember variability. However, the GMM unmixing models only have proper smoothness and sparsity prior constraints on the abundances ...
Yong Ma +6 more
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Remote Sensing, 2021 The accurate estimation of rice yield using remote sensing (RS) technology is crucially important for agricultural decision-making. The rice yield estimation model based on the vegetation index (VI) is commonly used when working with RS methods, however,
Ningge Yuan +7 more
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Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity
Remote Sensing, 2019 In recent years, endmember variability has received much attention in the field of hyperspectral unmixing. To solve the problem caused by the inaccuracy of the endmember signature, the endmembers are usually modeled to assume followed by a statistical ...
Qiwen Jin +7 more
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Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning
Advanced Intelligent Discovery, EarlyView.Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang +4 more
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Remote Sensing, 2019 Limited to the low spatial resolution of the hyperspectral imaging sensor, mixed pixels are inevitable in hyperspectral images. Therefore, to obtain the endmembers and corresponding fractions in mixed pixels, hyperspectral unmixing becomes a hot spot in ...
Yang Shao, Jinhui Lan
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Cytometry Part A, EarlyView.ABSTRACT Advances in spectral cytometry instrumentation and fluorescent reagents have led to the possibility of ultra‐high‐parameter panels exceeding 50 colors. However, panel size is limited in practice by unmixing‐dependent spreading (UDS), a phenomenon which leads to a progressive deterioration of unmixed signal‐to‐noise ratios in panels that ...
Peter L. Mage +2 more
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International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.ABSTRACT Numerical simulation of hydraulic fracturing remains challenging due to the strong coupling between geomechanics and fluid flow when modelling multiple physical mechanisms of rock deformation, fracture evolution and fluid leak‐off. This study develops a coupled hydraulic fracture propagation framework that combines the extended finite element ...
Ran Tao, Juliana Y. Leung, Samer Adeeb
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GIScience & Remote SensingTemporal unmixing, an extension of traditional spectral unmixing in a multi-temporal context, leverages endmembers defined by their temporal signatures to decompose mixed pixel responses into fractional cover.
Da Zhang, Chen Shi
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