Sensors, 2020 Multispectral photoacoustic imaging has been widely explored as an emerging tool to visualize and quantify tissue chromophores noninvasively. This modality can capture the spectral absorption signature of prominent tissue chromophores, such as oxygenated,
Valeria Grasso +2 more
doaj +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
Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery
Remote Sensing, 2018 Sparse unmixing has been successfully applied in hyperspectral remote sensing imagery analysis based on a standard spectral library known in advance. This approach involves reformulating the traditional linear spectral unmixing problem by finding the ...
Ruyi Feng, Lizhe Wang, Yanfei Zhong
doaj +1 more source
Spectral Unmixing with Sparsity and Structuring Constraints [PDF]
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018 This paper addresses the linear spectral unmixing problem, by incorporating different constraints that may be of interest in order to cope with spectral variability: sparsity (few nonzero abundances), group exclusivity (at most one nonzero abundance within subgroups of endmembers) and significance (non-zero abundances must exceed a threshold).
Ramzi Ben Mhenni +3 more
openaire +1 more source
Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies [PDF]
, 2023 Small water bodies (SWBs), such as ponds and on-farm reservoirs, are a key part of the hydrological system and play important roles in diverse domains from agriculture to conservation. The monitoring of SWBs has been greatly facilitated by medium-spatial-
Yalan Wang +11 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
JaiSubKU/spectral-unmixing: Spectral unmixing
, 2022 No description ...
Jai_lab_KU
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
SSANet: An Adaptive Spectral–Spatial Attention Autoencoder Network for Hyperspectral Unmixing
Remote Sensing, 2023 Convolutional neural-network-based autoencoders, which can integrate the spatial correlation between pixels well, have been broadly used for hyperspectral unmixing and obtained excellent performance.
Jie Wang +7 more
doaj +1 more source
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 ...
Moussaoui, Saïd +10 more
core +1 more source