Results 41 to 50 of about 296,705 (175)
Hyperspectral image unmixing using a multiresolution sticky HDP [PDF]
, 2012 This paper is concerned with joint Bayesian endmember extraction and linear unmixing of hyperspectral images using a spatial prior on the abundance vectors.We propose a generative model for hyperspectral images in which the abundances are sampled from a ...
Hero, Alfred O. +3 more
core +1 more source
Enhancing hyperspectral image unmixing with spatial correlations [PDF]
, 2010 This paper describes a new algorithm for hyperspectral image unmixing. Most unmixing algorithms proposed in the literature do not take into account the possible spatial correlations between the pixels.
Jean-Yves Tourneret +5 more
core +1 more source
Bayesian separation of spectral sources under non-negativity and full additivity constraints [PDF]
, 2009 This paper studied Bayesian algorithms for separating linear mixtures of spectral sources under non-negativity and full additivity constraints. These two constraints are required in some applications such as hyperspectral imaging and spectroscopy to get ...
Moussaoui, Saïd +3 more
core +1 more source
Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model [PDF]
, 2012 This paper studies a nonlinear mixing model for hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian
Altmann, Yoann +3 more
core +1 more source
Advanced Intelligent Discovery, EarlyView.Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto +3 more
wiley +1 more source
Remote Sensing, 2021 Hyperspectral image unmixing is an important task for remote sensing image processing. It aims at decomposing the mixed pixel of the image to identify a set of constituent materials called endmembers and to obtain their proportions named abundances ...
Jingyan Zhang +2 more
doaj +1 more source
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
wiley +1 more source
Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]
, 2014 We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known.
Se Un Parka +5 more
core +1 more source
Spatial Structural Priors for Sparse Unmixing of Remotely Sensed Hyperspectral Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingAs spectral libraries continue to expand, sparse unmixing has become essential for effectively interpreting mixed pixels in remotely sensed hyperspectral data.
Shaoquan Zhang +8 more
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral unmixing aims to separate pure materials and their corresponding proportions that constitute the mixed pixels of hyperspectral imagery (HSI). Recently, the matrix-vector nonnegative tensor factorization (MV-NTF) has attracted wide attention
Ping Yang +3 more
doaj +1 more source