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Adversarial Autoencoder Network for Hyperspectral Unmixing
IEEE Transactions on Neural Networks and Learning Systems, 2023Spectral unmixing (SU), which refers to extracting basic features (i.e., endmembers) at the subpixel level and calculating the corresponding proportion (i.e., abundances), has become a major preprocessing technique for the hyperspectral image analysis.
Qiwen Jin +5 more
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SPIE Proceedings, 2007
ABSTRACT Hyperspectral unmixing methods aim at the decompositio n of a hyperspectral image in to a collection endmembersignatures, i.e., the radiance or reectance of the materials present in the scene, and the correspondent abundancefractions at each pixel in the image.This paper introduces a new unmixing method termed dependent component analysis ...
Nascimento, Jose, Bioucas-Dias, José M.
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ABSTRACT Hyperspectral unmixing methods aim at the decompositio n of a hyperspectral image in to a collection endmembersignatures, i.e., the radiance or reectance of the materials present in the scene, and the correspondent abundancefractions at each pixel in the image.This paper introduces a new unmixing method termed dependent component analysis ...
Nascimento, Jose, Bioucas-Dias, José M.
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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.
Nascimento, Jose +4 more
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Compressed sensing based hyperspectral unmixing
2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014In 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.
Gürbüz, Ali Cafer +2 more
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Sparse distributed hyperspectral unmixing
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Blind hyperspectral unmixing is the task of jointly estimating the spectral signatures of material in a hyperspectral images and their abundances at each pixel. The size of hyperspectral images are usually very large, which may raise difficulties for classical optimization algorithms, due to limited memory of the hardware used.
Jakob Sigurdsson +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 hyperspectral intimate mixtures
SPIE Proceedings, 2010This paper addresses the unmixing of hyperspectral images, when intimate mixtures are present. In these scenarios the light suffers multiple interactions among distinct endmembers, which is not accounted for by the linear mixing model. A two-step method to unmix hyperspectral intimate mixtures is proposed: first, based on the Hapke intimate mixture
Nascimento, Jose, Bioucas-Dias, José M.
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