Results 21 to 30 of about 265 (159)

SCSU–GDO: Superpixel Collaborative Sparse Unmixing with Graph Differential Operator for Hyperspectral Imagery

Remote Sensing
In recent years, remarkable advancements have been achieved in hyperspectral unmixing (HU). Sparse unmixing, in which models mix pixels as linear combinations of endmembers and their corresponding fractional abundances, has become a dominant paradigm in ...
Kaijun Yang, Zhixin Zhao, Qishen Yang, Ruyi Feng   +3 more
doaj   +2 more sources

DSFC-AE: A New Hyperspectral Unmixing Method Based on Deep Shared Fully Connected Autoencoder

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The pervasive presence of mixed pixels in hyperspectral remote sensing imagery poses a substantial constraint on the quantitative progress of remote sensing technology. Hyperspectral unmixing (HU) techniques serve as effective means to address this issue.
Hao Chen, Tao Chen, Yuxiang Zhang, Bo Du, Antonio Plaza   +4 more
doaj   +2 more sources

A Global Spatial-Spectral Feature Fused Autoencoder for Nonlinear Hyperspectral Unmixing

Remote Sensing
Hyperspectral 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, Mingyu Yang, Hongyu Xie, Pinliang Yue, Wei Zhang, Qingbin Jiao, Liang Xu, Xin Tan   +7 more
doaj   +2 more sources

Sparsity-Constrained NMF Algorithm Based on Evolution Strategy for Hyperspectral Unmixing

MATEC Web of Conferences, 2018
As a powerful and explainable blind separation tool, non-negative matrix factorization (NMF) is attracting increasing attention in Hyperspectral Unmixing(HU).
Ning ShangBin, Zuo FengChao
doaj   +2 more sources

Adaptive Multiorder Graph Regularized NMF With Dual Sparsity for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral 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, Liangyu Liu, Xianchao Xiu, Wanquan Liu   +3 more
doaj   +2 more sources

Satellite Remote Sensing of Alpine Vegetation Dynamics: Challenges and Perspectives. [PDF]

Glob Chang Biol
Satellite greening has become a key tool for monitoring alpine vegetation change, but a positive vegetation‐index trend is not an ecological observation in itself. This perspective shows that interpreting alpine greening requires addressing two sequential challenges: methodological complexity, which can bias trends during image processing, and ...
Bayle A.
europepmc   +2 more sources

Spatial-spectral collaborative attention network for hyperspectral unmixing

Geocarto International
In recent years, the transformer architecture has demonstrated exceptional feature extraction capabilities in the field of computer vision (CV). Building on this, our paper aims to fully exploit the potential of the attention in transformers and apply it
Xiaojie Chen, Fanlei Meng, Ye Mo, Haixin Sun   +3 more
doaj   +2 more sources

Deep Learning Integration in Optical Microscopy: Advancements and Applications. [PDF]

Microsc Res Tech
It explores the integration of DL into optical microscopy, focusing on key applications including image classification, segmentation, and computational reconstruction. ABSTRACT Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye ...
Lahari PV, Dutta S, Deeksha H, Patel SA, Dehury B, Mazumder N.   +5 more
europepmc   +2 more sources

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions. [PDF]

Comput Intell Neurosci, 2022
Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. The current intelligent technologies, such as support vector machines, sparse representations, active learning, extreme learning machines, transfer learning, and deep learning, are typically based on the learning of the machines. These techniques
Datta D, Mallick PK, Bhoi AK, Ijaz MF, Shafi J, Choi J.   +5 more
europepmc   +2 more sources

Spatial Immunometabolism: Integrating Technologies to Decode Cellular Metabolism in Tissues. [PDF]

Eur J Immunol
This review highlights recent advances that enable spatially resolved analysis of immunometabolism within tissue microenvironments. Integrating mass spectrometry imaging, vibrational microscopy, and spatial omics reveals how metabolic organization shapes immune function in cancer and other pathologies.
Hartmann FJ.
europepmc   +2 more sources