A Generalized Multiscale Bundle-Based Hyperspectral Sparse Unmixing Algorithm [PDF]
IEEE Geoscience and Remote Sensing LettersIn hyperspectral sparse unmixing, a successful approach uses spectral bundles to address the variability of the endmembers (EMs) in the spatial domain. However, the regularization penalties usually used aggregate substantial computational complexity, and
L. C. Ayres +3 more
semanticscholar +1 more source
GPU Implementation of Graph-Regularized Sparse Unmixing With Superpixel Structures
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 To enhance spectral unmixing performance, a large number of algorithms have simultaneously investigated spatial and spectral information in hyperspectral images.
Zeng Li +3 more
semanticscholar +1 more source
Remote Sensing, 2021 Hyperspectral image unmixing is an important task for remote sensing image processing. It aims at decomposing the mixed pixel of the image to identify a set of constituent materials called endmembers and to obtain their proportions named abundances ...
Jingyan Zhang +2 more
doaj +1 more source
Self-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search are Related [PDF]
, 2015 This paper considers a recently emerged hyperspectral unmixing formulation based on sparse regression of a self-dictionary multiple measurement vector (SD-MMV) model, wherein the measured hyperspectral pixels are used as the dictionary.
Bioucas-Dias, José M. +3 more
core +2 more sources
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
, 2016 In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem.
Chen, Badong +4 more
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Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation [PDF]
, 2019 In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors.
Chen, Xiuwan +4 more
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Clustered Multitask Nonnegative Matrix Factorization for Spectral Unmixing of Hyperspectral Data
, 2019 In this paper, the new algorithm based on clustered multitask network is proposed to solve spectral unmixing problem in hyperspectral imagery. In the proposed algorithm, the clustered network is employed.
Khoshsokhan, Sara +2 more
core +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral unmixing aims to separate pure materials and their corresponding proportions that constitute the mixed pixels of hyperspectral imagery (HSI). Recently, the matrix-vector nonnegative tensor factorization (MV-NTF) has attracted wide attention
Ping Yang +3 more
doaj +1 more source
Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery [PDF]
, 2007 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 ...
Chein-i Chang +4 more
core +6 more sources
A Multiple Hypothesis Testing Approach to Low-Complexity Subspace Unmixing [PDF]
, 2016 Subspace-based signal processing traditionally focuses on problems involving a few subspaces. Recently, a number of problems in different application areas have emerged that involve a significantly larger number of subspaces relative to the ambient ...
Bajwa, Waheed U., Mixon, Dustin G.
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