Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery [PDF]
, 2008 This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive ...
Chang, Chein-I +5 more
core +1 more source
Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image [PDF]
, 2013 Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction
Eches, Olivier +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
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
Model-inspired deep neural networks for hyperspectral unmixing
, 2022 Model-based and learning-based methods are two typical classes for hyperspectral unmixing. Model-based methods are interpretable but rely on the definition of a physical model and iterative optimization. Learning-based methods have high learning ability,
Xiong, F, Zhou, J, Ye, M, Qian, Y
core +1 more source
Hyperspectral Images Unmixing Based on Abundance Constrained Multi-Layer KNMF
IEEE Access, 2021 Due to the low spatial resolution of the sensors, the hyperspectral images contain mixed pixels. The purpose of hyperspectral unmixing is to decompose the mixed pixels into a series of endmembers and abundance fractions.
Jing Liu, You Zhang, Yi Liu, Caihong Mu
doaj +1 more source
Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery [PDF]
, 2010 This paper studies a new Bayesian unmixing algorithm for hyperspectral images. Each pixel of the image is modeled as a linear combination of so-called endmembers.
Eches, Olivier +3 more
core +1 more source
Estimating the number of endmembers in hyperspectral images using the normal compositional model and a hierarchical Bayesian algorithm. [PDF]
, 2010 This paper studies a semi-supervised Bayesian unmixing algorithm for hyperspectral images. This algorithm is based on the normal compositional model recently introduced by Eismann and Stein.
Eches, Olivier +2 more
core +1 more source
ADVANCES IN HYPERSPECTRAL AND MULTISPECTRAL IMAGE FUSION AND SPECTRAL UNMIXING [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 In this work, we jointly process high spectral and high geometric resolution images and exploit their synergies to (a) generate a fused image of high spectral and geometric resolution; and (b) improve (linear) spectral unmixing of hyperspectral ...
C. Lanaras, E. Baltsavias, K. Schindler
doaj +1 more source
Hyperspectral Unmixing Hierarchies
CoRRUnmixing reveals the spatial distribution and spectral details of different constituents, called endmembers, in a hyperspectral image. Because unmixing has limited ground truth requirements, can accommodate mixed pixels, and is closely tied to light propagation, it is a uniquely powerful tool for analyzing hyperspectral images.
Joseph L. Garrett +6 more
openaire +2 more sources