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Compressed sensing based hyperspectral unmixing
2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014 In hyperspectral images the measured spectra for each pixel can be modeled as convex combination of small number of endmember spectra. Since the measured structure contains only a few of possible responses out of possibly many materials sparsity based convex optimization techniques or compressive sensing can be used for hyperspectral unmixing.
R. Tufan Albayrak +2 more
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Sparse Distributed Multitemporal Hyperspectral Unmixing
IEEE Transactions on Geoscience and Remote Sensing, 2017Blind hyperspectral unmixing jointly estimates spectral signatures and abundances in hyperspectral ima-ges (HSIs). Hyperspectral unmixing is a powerful tool for analyzing hyperspectral data. However, the usual huge size of HSIs may raise difficulties for classical unmixing algorithms, namely, due to limitations of the hardware used.
Jóhannes R. Sveinsson +2 more
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Manifold Regularized Sparse NMF for Hyperspectral Unmixing
IEEE Transactions on Geoscience and Remote Sensing, 2013 Hyperspectral unmixing is one of the most important techniques in analyzing hyperspectral images, which decomposes a mixed pixel into a collection of constituent materials weighted by their proportions.
Yuan Yuan, Xuelong Li, Xiaoqiang Lu
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Parallel Hyperspectral Unmixing on GPUs
IEEE Geoscience and Remote Sensing Letters, 2014This letter presents a new parallel method for hyperspectral unmixing composed by the efficient combination of two popular methods: vertex component analysis (VCA) and sparse unmixing by variable splitting and augmented Lagrangian (SUNSAL). First, VCA extracts the endmember signatures, and then, SUNSAL is used to estimate the abundance fractions.
José M. P. Nascimento +4 more
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Cauchy NMF for Hyperspectral Unmixing
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020Non-negative matrix factorization (NMF) is a classical hyperspectral unmixing model which minimizes the Euclidean distance between the hyperspectral data matrix and its low rank approximation (i.e., the product of endmember matrix and abundance matrix), and it fails when applied to noisy data because the loss function is sensitive to outliers.
Jiangtao Peng +3 more
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Semi-supervised hyperspectral unmixing
2014 IEEE Geoscience and Remote Sensing Symposium, 2014In this paper, an effective method is proposed that combines supervised and unsupervised unmixing. We assume a linear model for the hyperspectral data and incorporate information about endmembers that are known to be in the data into the model. This information can be acquired from a spectral library or extracted from the data.
Jakob Sigurdsson +2 more
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Fast multitemporal hyperspectral unmixing
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017In this paper, we present a fast blind multitemporal hyperspectral unmixing algorithm, using an l 1 penalty to promote sparse abundances. The method is able to account for different acquisition conditions of multitemporal images, by allowing the spectral signatures in the different temporal images to vary. The new algorithm is tested on simulated data
Jakob Sigurdsson +2 more
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Unmixing sparse hyperspectral mixtures
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or atoms), is a hard combinatorial problem which, as in other areas, has been increasingly addressed. This paper studies the efficiency of the sparse regression techniques in the
Marian-Daniel Iordache +2 more
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Superpixel construction for hyperspectral unmixing
2018 26th European Signal Processing Conference (EUSIPCO), 2018Spectral unmixing aims to determine the component materials and their associated abundances from mixed pixels in a hyperspectral image. Instead of performing unmixing independently on each pixel, investigating spatial and spectral correlations among pixels can be beneficial to enhance the unmixing performance.
Zeng Li 0001 +2 more
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