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Spatially Adaptive Hyperspectral Unmixing
IEEE Transactions on Geoscience and Remote Sensing, 2011Spectral unmixing is a common task in hyperspectral data analysis. In order to sufficiently spectrally unmix the data, three key steps must be accomplished: Estimate the number of endmembers (EMs), identify the EMs, and then unmix the data. Several different statistical and geometrical approaches have been developed for all steps of the unmixing ...
Kelly Canham +4 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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On Diverse Noises in Hyperspectral Unmixing
IEEE Transactions on Geoscience and Remote Sensing, 2015Traditional spectral unmixing methods are usually based on the linear mixture model (LMM) or nonlinear mixture model (NLMM), in which only the additive noise is considered. However, in hyperspectral applications, the additive, multiplicative, and mixed noises play important roles. In this paper, we propose an antinoise model for hyperspectral unmixing.
Chunzhi Li +2 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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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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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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Segmentation-based cNMF for hyperspectral unmixing
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017This paper presents a modification to the cNMF for unmixing where the image is first segmented and the cNMF is applied to individual segments for endmember extraction. Extracted spectral endmembers from individual segments are clustered in endmember classes to describe the entire image. The approach is compared with the global cNMF.
Mohammed Q. Alkhatib, Miguel Velez-Reyes
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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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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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