Results 31 to 40 of about 296,705 (175)
IVIU-Net: Implicit Variable Iterative Unrolling Network for Hyperspectral Sparse Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 At present, an emerging technique called the algorithm unrolling approach has attracted wide attention, because it is capable of developing efficient and interpretable layers to eliminate the black-box nature of deep learning (DL).
Yuantian Shao, Qichao Liu, Liang Xiao
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Nonlinear unmixing of hyperspectral images using a generalized bilinear model [PDF]
, 2011 Nonlinear models have recently shown interesting properties for spectral unmixing. This paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization not only of
Altmann, Yoann +9 more
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Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery
Remote Sensing, 2018 Sparse unmixing has been successfully applied in hyperspectral remote sensing imagery analysis based on a standard spectral library known in advance. This approach involves reformulating the traditional linear spectral unmixing problem by finding the ...
Ruyi Feng, Lizhe Wang, Yanfei Zhong
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Hyperspectral Unmixing Via Nonconvex Sparse and Low-Rank Constraint
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 In recent years, sparse unmixing has attracted significant attention, as it can effectively avoid the bottleneck problems associated with the absence of pure pixels and the estimation of the number of endmembers in hyperspectral scenes.
Hongwei Han +7 more
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Sparse and Low-Rank Constrained Tensor Factorization for Hyperspectral Image Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Third-order tensors have been widely used in hyperspectral remote sensing because of their ability to maintain the 3-D structure of hyperspectral images.
Pan Zheng, Hongjun Su, Qian Du
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IEEE Access, 2021 Hyperspectral unmixing has attracted considerable attentions in recent years and some promising algorithms have been developed. In this paper, collaborative representation–based unmixing (CRU) for hyperspectral images is proposed.
Jing Wang
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Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery [PDF]
, 2008 This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive ...
Chang, Chein-I +5 more
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Unsupervised Bayesian linear unmixing of gene expression microarrays [PDF]
, 2013 Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for
Ginsburg, Geoffrey S +8 more
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Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image [PDF]
, 2013 Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction
Eches, Olivier +3 more
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NMF-SAE: An Interpretable Sparse Autoencoder for Hyperspectral Unmixing
, 2021 Hyperspectral unmixing is an important tool to learn the material constitution and distribution of a scene. Model-based unmixing methods depend on well-designed iterative optimization algorithms, which is usually time consuming.
Jun Zhou +9 more
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