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 ...
Alfred O. Hero +5 more
core +9 more sources
Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI).
Xin-Ru Feng +5 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 of the unmixing algorithms proposed in the literature do not take into account the possible spatial correlations between the pixels.
Dobigeon, Nicolas +2 more
core +6 more sources
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
Spectral unmixing of Multispectral Lidar signals [PDF]
, 2015 In this paper, we present a Bayesian approach for spectral unmixing of multispectral Lidar (MSL) data associated with surface reflection from targeted surfaces composed of several known materials.
Altmann, Yoann +2 more
core +2 more sources
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
, 2016 In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem.
Chen, Badong +4 more
core +2 more sources
Further results on dissimilarity spaces for hyperspectral images RF-CBIR [PDF]
, 2013 Content-Based Image Retrieval (CBIR) systems are powerful search tools in image databases that have been little applied to hyperspectral images. Relevance feedback (RF) is an iterative process that uses machine learning techniques and user's feedback to ...
Datcu, Mihai +2 more
core +3 more sources
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
Unmixing of Hyperspectral Data Using Robust Statistics-based NMF
, 2012 Mixed pixels are presented in hyperspectral images due to low spatial resolution of hyperspectral sensors. Spectral unmixing decomposes mixed pixels spectra into endmembers spectra and abundance fractions.
Ghassemian, Hassan, Rajabi, Roozbeh
core +1 more source
Clustered Multitask Nonnegative Matrix Factorization for Spectral Unmixing of Hyperspectral Data
, 2019 In this paper, the new algorithm based on clustered multitask network is proposed to solve spectral unmixing problem in hyperspectral imagery. In the proposed algorithm, the clustered network is employed.
Khoshsokhan, Sara +2 more
core +1 more source