Results 61 to 70 of about 6,602 (196)

Variability of the endmembers in spectral unmixing: Recent advances [PDF]

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016
Endmember variability has been identified as one of the main limitations of the usual Linear Mixing Model, conventionally used to perform spectral unmixing of hyperspectral data. The topic is currently receiving a lot of attention from the community, and many new algorithms have recently been developed to model this variability and take it into account.
Drumetz, Lucas, Chanussot, Jocelyn, Jutten, Christian   +2 more
openaire   +2 more sources

Gαi1/3 Are Essential for BMP2‐Induced Osteogenesis by Bridging BMP Receptors to the Gab1 Signaling Complex

Advanced Science, EarlyView.
The Gαi1/3 protein is a crucial connecting protein closely related to the signal transduction process of bone formation‐related cytokines such as BMP2. Gαi1/3 mediates downstream signaling pathways such as Smads and MAPK by binding to adaptor proteins like Gab1, thereby promoting the osteogenic effect of BMSCs.
Huajian Shan, Chaowen Bai, Yifei Zhu, Chenyang Xu, Sonngtao Li, Qize Sun, Chang She, Jinyu Bai, Cong Cao, Yitian Yang, Xiaozhong Zhou   +10 more
wiley   +1 more source

Graph-Based Adaptive Network With Spatial-Spectral Features for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing aims to extract basic material (endmember) spectra and estimate their corresponding fractions (abundances) from observed pixels in hyperspectral images (HSIs).
Hua Dong, Xiaohua Zhang, Jinhua Zhang, Hongyun Meng, Licheng Jiao   +4 more
doaj   +1 more source

A Spectral Unmixing Method by Maximum Margin Criterion and Derivative Weights to Address Spectral Variability in Hyperspectral Imagery

Remote Sensing, 2019
Limited to the low spatial resolution of the hyperspectral imaging sensor, mixed pixels are inevitable in hyperspectral images. Therefore, to obtain the endmembers and corresponding fractions in mixed pixels, hyperspectral unmixing becomes a hot spot in ...
Yang Shao, Jinhui Lan
doaj   +1 more source

Intranasal Delivery of Gallium–Quercetin Nanoparticles for Multi‐Target Ferroptosis Inhibition in Parkinson's Disease

Advanced Science, EarlyView.
Leveraging the chemical similarity between Ga3+ and Fe3+ as well as the antioxidant properties of quercetin, gallium–quercetin nanoparticles (GQNPs) were prepared to integrate iron homeostasis regulation, oxidative stress suppression, and mitochondrial protection for multi‐target ferroptosis inhibition in Parkinson's Disease.
Keyang Xu, Dandan Kou, Xiao Xiao, Bingbing Bai, Caide Liu, Mengyao Wu, Yadan Shi, Mengdan Xu, Xiaoyi Cao, Haodi Qi, Shuhua Wu, Jianfeng Zeng, Mingyuan Gao   +12 more
wiley   +1 more source

A Multilinear Mixing Model for Nonlinear Spectral Unmixing [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2016
In hyperspectral unmixing, bilinear and linear–quadratic models have become popular recently, and also the polynomial postnonlinear model shows promising results. These models do not consider endmember interactions involving more than two endmembers, although such interactions might compose a nontrivial part of the observed spectrum in scenarios ...
Rob Heylen, Paul Scheunders
openaire   +1 more source

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source

Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing

Remote Sensing, 2019
Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression.
Ruyi Feng, Lizhe Wang, Yanfei Zhong
doaj   +1 more source

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

A Global-to-Local Spectral-Spatial Attention-Based Nonlinearity and Scaled Endmember Variability Parametric Learning Network for Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing has attracted increasing attention in remote sensing applications. Unfortunately, significant unmixing residuals often arise from the coupled nonlinear mixing effects and spectral variability (SV), bringing challenges for reliably ...
Yi Zhao, Bin Yang
doaj   +1 more source