Remote Sensing, 2019 Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression.
Ruyi Feng, Lizhe Wang, Yanfei Zhong
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Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery [PDF]
, 2011 This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian noise.
Altmann, Yoann +9 more
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Simultaneous Nonconvex Denoising and Unmixing for Hyperspectral Imaging
IEEE Access, 2019 Sparse hyperspectral unmixing aims at finding the sparse fractional abundance vector of a spectral signature present in a mixed pixel. However, there are several types of noise present in the hyperspectral images.
Taner Ince, Tugcan Dundar
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Curvelet Transform Domain-Based Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Hyperspectral unmixing (HU) is an efficient way to extract component information from mixed pixels in remotely sensed imagery. Nonnegative matrix factorization (NMF) based unmixing methods have been widely used due to their ability to extract endmembers (
Xiang Xu +3 more
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A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for
Qiqi Zhu +4 more
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Nonlinear unmixing of hyperspectral images: Models and algorithms [PDF]
, 2013 When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM).
McLaughlin, Stephen; id_orcid +14 more
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Bayesian Orthogonal Component Analysis for Sparse Representation [PDF]
, 2010 This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This undercomplete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with ...
Nicolas Dobigeon +3 more
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A Multiobjective Group Sparse Hyperspectral Unmixing Method With High Correlation Library
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral sparse unmixing aims at modeling pixels of hyperspectral image as a linear combination of a subset of a prior spectral library. Over the past years, spectral library has been constantly expanded, including spectra of the same material with ...
Yanyi Wei +4 more
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TARGET TRANSFORMATION CONSTRAINED SPARSE UNMIXING (TTCSU) ALGORITHM FOR RETRIEVING HYDROUS MINERALS ON MARS: APPLICATION TO SOUTHWEST MELAS CHASMA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 Quantitative analysis of hydrated minerals from hyperspectral remote sensing data is fundamental for understanding Martian geologic process. Because of the difficulties for selecting endmembers from hyperspectral images, a sparse unmixing algorithm has ...
H. Lin +6 more
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Joint-Sparse-Blocks Regression for Total Variation Regularized Hyperspectral Unmixing
IEEE Access, 2019 Sparse unmixing has attracted much attention in recent years. It aims at estimating the fractional abundances of pure spectral signatures in mixed pixels in hyperspectral images.
Jie Huang +3 more
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