Results 91 to 100 of about 296,705 (175)
SUnAA: Sparse Unmixing using Archetypal Analysis
, 2023 International audienceThis paper introduces a new sparse unmixing technique using archetypal analysis (SUnAA). First, we design a new model based on archetypal analysis.
Rasti, Behnood +3 more
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Sparse unmixing using deep convolutional networks
, 2022 : This paper proposes a sparse unmixing technique using a convolutional neural network (SUnCNN). We reformulate the sparse unmixing problem into an optimization over the parameters of a convolutional network.
Scheunders, Paul +2 more
core
Implementation strategies for hyperspectral unmixing using Bayesian source separation. [PDF]
, 2010 Positive Source Separation (BPSS) is a useful unsupervised approach for hyperspectral data unmixing, where numerical non-negativity of spectra and abundances has to be ensured, such in remote sensing. Moreover, it is sensible to impose a sum-to-one (full
Moussaoui, Saïd +11 more
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Total Utility Metric Based Dictionary Pruning for Sparse Hyperspectral Unmixing
, 2021 Given a spectral library, sparse unmixing aims to estimate the fractional proportions in each pixel of a hyperspectral image scene. However, the ever-growing dimensionality of spectral dictionaries strongly limits the performance of sparse unmixing ...
KÜÇÜK, SEFA +1 more
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Manifold regularized sparse NMF for hyperspectral unmixing
, 2013 Hyperspectral unmixing is one of the most important techniques in analyzing hyperspectral images, which decomposes a mixed pixel into a collection of constituent materials weighted by their proportions.
Wu, H. +8 more
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Spatial–Spectral Multiscale Sparse Unmixing for Hyperspectral Images
, 2023 International audienceWe propose a simple yet efficient sparse unmixing method for hyperspectral images. It exploits the spatial and spectral properties of hyperspectral images by designing a new regularization informed by multiscale analysis.
Dobigeon, Nicolas, Ince, Taner
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Deblurring and sparse unmixing for hyperspectral images
, 2013 The 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 ...
Huang, Ting Zhu +4 more
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Synthesizing hyperspectral Data using generative Models to train spectral Unmixing Methods for low-cost Crop Residue Cover Mapping [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesThe spectral range of the Field Imaging Nanosatellite for Crop residue Hyperspectral Mapping (FINCH), developed by the University of Toronto Aerospace Team’s Space System Division, poses significant challenges for hyperspectral unmixing to ...
E. Artan +9 more
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingImpervious surface abundance (ISA) is an important indicator for monitoring urbanization and environmental disaster management processes. Commonly used spectral unmixing techniques extract ISA in the form of mixed pixels; however, in previous ...
Yanze Liu +4 more
doaj +1 more source
Sparse Unmixing With Dictionary Pruning for Hyperspectral Change Detection
, 2017 The localization of changes that occur between the images in a multitemporal series is crucial for many applications, ranging from environmental monitoring to military surveillance.
Iordache, Marian-Daniel +2 more
core +1 more source