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Joint hyperspectral unmixing for urban computing
GeoInformatica, 2019Recently, many methods for hyperspectral unmixing have been proposed. These methods are often based on nonnegative matrix factorization (NMF), which naturally inherits the non-negative advantage and is in line with the common sense of physics. Although there are many ways to perform NMF-based hyperspectral unmixing, these methods can only unmix one ...
Jihai Yang +3 more
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Reweighted Sparse Regression for Hyperspectral Unmixing
IEEE Transactions on Geoscience and Remote Sensing, 2016Hyperspectral unmixing (HSU) plays an important role in hyperspectral image (HSI) analysis. Recently, the HSU method based on sparse regression has drawn much attention. This paper presents a new weighted sparse regression problem for HSU and proposes two iterative reweighted algorithms for solving this problem, where the weights used for the next ...
Cheng Yong Zheng +3 more
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Robust sparse unmixing of hyperspectral data
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Sparse unmixing (SU) of hyperspectral data has recently received particular attention for analyzing remote sensing images, which aims at finding the optimal subset of signatures to best model the mixed pixel in the scene. However, most SU methods are based on the commonly admitted linear mixing model (LMM), which ignores the possible nonlinear effects (
Yong Ma 0001 +2 more
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Sparse filtering based hyperspectral unmixing
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016This work proposes a hyperspectral unmixing technique based on sparse filtering approach. The proposed method exploits the sparsity of feature distribution rather than modeling the data distribution. The proposed sparse filtering based unmixing procedure is essentially parameter-free, and the only parameter is to find the number of endmembers to be ...
Hemant Kumar Aggarwal, Angshul Majumdar
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Hyperspectral images unmixing with rare signals
2016 6th European Workshop on Visual Information Processing (EUVIP), 2016Pixels in hyperspectral images are a mixing of source signals. Hyperspectral unmixing is an important issue in image processing. In this paper we consider a linear mixing model. We address the unmixing issue when some "rare" source signals are only present in few mixed pixels.
Ravel, Sylvain +2 more
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Deblurring and Sparse Unmixing for Hyperspectral Images
IEEE Transactions on Geoscience and Remote Sensing, 2013The main aim of this paper is to study total variation (TV) regularization in deblurring and sparse unmixing of hyperspectral images. In the model, we also incorporate blurring operators for dealing with blurring effects, particularly blurring operators for hyperspectral imaging whose point spread functions are generally system dependent and formed ...
Xi-Le Zhao +4 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.
Jakob Sigurdsson +3 more
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Variational methods for spectral unmixing of hyperspectral unmixing
2011International ...
Eches, Olivier +3 more
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A Dataset with Ground-Truth for Hyperspectral Unmixing
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Spectral unmixing is one of the most important issues of hyperspectral data processing. However, the lack of publicly available dataset with ground-truth makes it difficult to evaluate and compare the performance of unmixing algorithms. In this work, we create several experimental scenes in our laboratory with controlled settings where the pure ...
Min Zhao 0014, Jie Chen 0022
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Random Hadamard Projections for Hyperspectral Unmixing
IEEE Geoscience and Remote Sensing Letters, 2017Dimensionality reduction based on random projections is investigated in the context of spectral unmixing of hyperspectral imagery with aims toward unmixing accuracy and computational efficiency. To this end, both Hadamard-based random projections—which significantly reduce computational costs with respect to more traditional Gaussian-driven projections—
Vineetha Menon +2 more
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