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The Potential Role of Hyperspectral Retinal Imaging of Choroidal Tumors. [PDF]
Ocul Oncol PatholDang D, Hadoux X, O'Day R.
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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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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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Structured Sparse Method for Hyperspectral Unmixing [PDF]
ISPRS Journal of Photogrammetry and Remote Sensing, 2014 Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data.
Feiyun Zhu, Shiming Xiang, Bin Fan
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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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An Antinoise Method for Hyperspectral Unmixing
IEEE Geoscience and Remote Sensing Letters, 2015In this letter, we propose an antinoise method for hyperspectral unmixing. In the antinoise method, all noises are addressed. The following techniques are applied: 1) an endmember dictionary is constructed first to initialize the solution; 2) an approximated L 0 norm constraint is employed to prune the dictionary and fulfill the sparse coding; and 3)
Chunzhi Li +3 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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