Results 91 to 100 of about 309,259 (214)
Smooth and Sparse Regularization for NMF Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017 In this paper, we propose a matrix factorization method for hyperspectral unmixing using the linear mixing model. In this method, we add the $\arctan$ functions of the endmembers to the $\ell _2$ -norm of the error in order to exploit the sparse property of the fractional abundances.
Yaser Esmaeili Salehani, Saeed Gazor
openaire +2 more sources
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingSparse unmixing, which introduces a large spectral library to transform the problem of mixed-pixel decomposition into the search for an optimal subset of endmembers that best represents the spectral characteristics of an image, has become a prominent ...
Fan Li +8 more
doaj +1 more source
Non‐invasive Imaging Modalities for Breslow Thickness Estimation in Melanoma: A Scoping Review
Skin Research and Technology, Volume 32, Issue 10, October 2026.ABSTRACT Background Breslow thickness (BT) is the most important prognostic biomarker in cutaneous melanoma and directly influences surgical management, sentinel lymph node biopsy decisions and survival outcomes. Several non‐invasive imaging modalities have been investigated for preoperative BT estimation; however, their relative performance and ...
Biagio Scotti +6 more
wiley +1 more source
SUnCNN : sparse unmixing using unsupervised convolutional neural network
, 2021 : In this letter, we propose a sparse unmixing technique using a convolutional neural network (SUnCNN) for hyperspectral images. SUnCNN is the first deep learning-based technique proposed for sparse unmixing.
Koirala, Bikram +3 more
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Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing
Remote Sensing, 2018 Hyperspectral unmixing, aiming to estimate the fractional abundances of pure spectral signatures in each mixed pixel, has attracted considerable attention in analyzing hyperspectral images.
Si Wang +4 more
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Advanced Materials, Volume 38, Issue 51, 11 September 2026.AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll +19 more
wiley +1 more source
Hyperspectral unmixing with Sparse Group Lasso
, 2011 Sparse unmixing has been recently introduced as a mecha-nism to characterize mixed pixels in remotely sensed hyper-spectral images. It assumes that the observed image signa-tures can be expressed in the form of linear combinations of a number of pure ...
Marian-daniel Iordache +3 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThe mixed pixel problem, arising from the complex vegetation types of peatlands, poses a significant challenge for remote sensing-based peatland mapping.
Yulin Xu, Xiaodong Na
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Environmetrics, Volume 37, Issue 6, September 2026.ABSTRACT The formation of ground‐level ozone follows complex nonlinear photochemical processes that depend on multiple environmental factors and have strong spatio‐temporal structures. Environmental data used to study these dynamics usually originate from multiple sources, including in situ monitoring stations and satellite observations.
Mika Sipilä +5 more
wiley +1 more source
Hyperspectral Sparse Unmixing via Nonconvex Shrinkage Penalties
, 2023 International audienceHyperspectral sparse unmixing aims at finding the optimal subset of spectral signatures in the given spectral library and estimating their proportions in each pixel.
Hong, Danfeng +11 more
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