Spectral unmixing

By Nirmal Keshava, J. F. Mustard. IEEE Signal Processing Magazine, 2002

2,496 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/70a898b7a620b89ebeaa81f9f6df7702836a2ed6

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Citations in the corpus

Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations (2018, background): “Spectral unmixing is still a challenging problem in the realm of hyperspectral image processing [1], [46], [47].”
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery (2009, background): “Moreover, in (1), np = [np,1, . . . , np,L] T is an additive noise sequence which is assumed to be an independent and identically distributed (i.i.d.) zero-mean Gaussian sequence with covariance matrix Σn = σ2IL, where IL is the identity matrix of dimension L× L np ∼ N (0L,Σn) .”
Retrieval of subpixel Tamarix canopy cover from Landsat data along the Forgotten River using linear and nonlinear spectral mixture models (2010, methodology): “The relative contribution of component spectra is then determined by the inversion of mixture models (Chang & Heinz, 2000; Heinz & Chang, 2001; Hu et al., 1999; Keshava & Mustard, 2002).”
Hyperspectral Super-Resolution by Coupled Spectral Unmixing (2015, background): “According to the linear mixing model [17, 7] the intensities z ∈ R at a given pixel of Z are described by an additive mixture”
Multitask Diffusion Adaptation Over Networks (2013, background): “In hyperspectral images, a pixel is usually a spectral mixture of several spectral signatures of pure materials, termed endmembers, due to limited spatial resolution of devices and diversity of materials [60].”
Manifold Regularized Sparse NMF for Hyperspectral Unmixing (2013, background): “2213825 of constituent materials (also called endmembers) and their relative proportions (also called abundances) [3], [4].”
A Review of Nonlinear Hyperspectral Unmixing Methods (2014, background): “Another problem aggravating the spectral unmixing problem is that the number of endmembers M is usually unknown, and has to be estimated by some means.”
Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing (2009, background): “process is a powerful and typical model for representing the synthesis of mixed pixels from distinct endmembers [3].”
Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations (2005, background): “It should be noted that the SAD measurement is invariant in the multiplication of the input vectors by constants and, consequently, is invariant to unknown multiplicative scalings that may arise due to differences in illumination and sensor observation angle [32].”
Bayesian sparse representation for hyperspectral image super resolution (2015, methodology): “[35] have used hyperspectral unmixing [19], [3] for spatial resolution enhancement of hyperspectral images.”
Change Detection from Temporal Sequences of Class Labels: Application to Land Cover Change Mapping (2013, background): “Methods in remote sensing literature use spectral unmixing approaches [9] to find the fractional composition of mixed pixels in terms of pure classes.”
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, background): “According to Bioucas-Dias [54] spectral unmixing (see, for example, [74,75]) purpose is to restore the ability of ascertain materials, i.”
Recent Advances in Techniques for Hyperspectral Image Processing (2009, methodology): “A doi:10.1016/j.rse.2007.07.028 a b s t r a c t a r t i c l e i n f o Article history: Received 21 July 2006 Received in revised form 25 June 2007 Accepted 27 July 2007 Keywords: Classification Hyperspectral imaging Kernel methods Support vector machines Markov random fields Mathematical morphology…”
Earth system science related imaging spectroscopy — an assessment (2009, methodology): “Imaging spectroscopy has not only enabled mapping parts of the Earth system with unprecedented accuracy (Adams et al., 1989; Ifarraguerri & Chang, 1999; Lee et al., 2004; Rast et al., 2004), but also significantly contributed to the mapping of its ecotones (Lucas & Carter, 2008) and other transition”
Hyperspectral Subspace Identification (2008, methodology): “, [5]–[13]), the spectral vectors are a linear combination of the”