Results 81 to 90 of about 1,956 (163)
A Global Spatial-Spectral Feature Fused Autoencoder for Nonlinear Hyperspectral Unmixing
Remote SensingHyperspectral unmixing (HU) aims to decompose mixed pixels into a set of endmembers and corresponding abundances. Deep learning-based HU methods are currently a hot research topic, but most existing unmixing methods still rely on per-pixel training or ...
Mingle Zhang +7 more
doaj +1 more source
IEEE Access, 2019 Nonnegative matrix factorization (NMF) is a powerful tool for hyperspectral unmixing (HU). This method factorizes a hyperspectral cube into constituent endmembers and their fractional abundances.
Li Sun +3 more
doaj +1 more source
Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification [PDF]
, 2018 Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost ...
Hong, Danfeng +3 more
core +1 more source
Hyperspectral Analysis of Oil and Oil-Impacted Soils for Remote Sensing Purposes [PDF]
, 2007 While conventional multispectral sensors record the radiometric signal only at a handful of wavelengths, hyperspectral sensors measure the reflected solar signal at hundreds contiguous and narrow wavelength bands, spanning from the visible to the ...
ANDREOLI Giovanni +3 more
core
Image patch analysis of sunspots and active regions. II. Clustering via matrix factorization
, 2015 Separating active regions that are quiet from potentially eruptive ones is a key issue in Space Weather applications. Traditional classification schemes such as Mount Wilson and McIntosh have been effective in relating an active region large scale ...
De Visscher, Ruben +5 more
core +1 more source
SSTNT: A Spatial–Spectral Similarity Guided Transformer-in-Transformer for Hyperspectral Unmixing
PhotonicsVision Transformers (ViTs), owing to their strong capability in modeling global contextual dependencies, have been widely adopted in hyperspectral image unmixing (HU).
Xinyu Cui +3 more
doaj +1 more source
Molecular Oncology, Volume 19, Issue S1, Page 1-940, June 2025.Abstracts submitted to the ‘EACR 2025 Congress: Innovative Cancer Science’, from 16–19 June 2025 and accepted by the Congress Organising Committee are published in this Supplement of Molecular Oncology, an affiliated journal of the European Association for Cancer Research (EACR).
wiley +1 more source
State-of-the-art and gaps for deep learning on limited training data in remote sensing
, 2018 Deep learning usually requires big data, with respect to both volume and variety. However, most remote sensing applications only have limited training data, of which a small subset is labeled.
Anderson, Derek T. +2 more
core +1 more source
Adaptive Multiorder Graph Regularized NMF With Dual Sparsity for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHyperspectral unmixing (HU) is a critical yet challenging task in remote sensing. However, existing nonnegative matrix factorization (NMF) methods with graph learning mostly focus on first-order or second-order nearest neighbor relationships and usually ...
Hui Chen +3 more
doaj +1 more source
Randomized Nonnegative Matrix Factorization
, 2018 Nonnegative matrix factorization (NMF) is a powerful tool for data mining. However, the emergence of `big data' has severely challenged our ability to compute this fundamental decomposition using deterministic algorithms. This paper presents a randomized
Erichson, N. Benjamin +3 more
core +1 more source