Results 41 to 50 of about 6,533 (191)

Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection

Remote Sensing, 2021
Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal.
Genping Zhao, Fei Li, Xiuwei Zhang, Kati Laakso, Jonathan Cheung-Wai Chan   +4 more
doaj   +1 more source

Deep Spectral Convolution Network for Hyperspectral Unmixing [PDF]

2018 25th IEEE International Conference on Image Processing (ICIP), 2018
In this paper, we propose a novel hyperspectral unmixing technique based on deep spectral convolution networks (DSCN). Particularly, three important contributions are presented throughout this paper. First, fully-connected linear operation is replaced with spectral convolutions to extract local spectral characteristics from hyperspectral signatures ...
Savas Özkan, Gozde Bozdagi Akar
openaire   +2 more sources

PD‐1 Inhibits CD4+ TRM‐Mediated cDC1 Mobilization via Suppressing JAML in Human NSCLC

Advanced Science, EarlyView.
CD4+ tissue‐resident memory T cells (TRMs) in non‐small cell lung cancer recruit conventional type 1 dendritic cells via XCL1‐XCR1 signaling, orchestrating antitumor immunity. The costimulatory molecule JAML is essential for this process. PD‐1 blockade restores JAML expression and cDC1 mobilization, while JAML agonists synergize with anti‐PD‐1 therapy,
Zheyu Shao, Qinyuan Liu, Zhongwei Xin, Zhiyao Zhou, Mingjie Lin, Di Chen, Zhixing Hao, Yongyuan Chen, Wenxuan Wu, Shuyang Zhang, Xiaoke Chen, Xia Xu, Jinfan Li, Wei Lin, Yuhao Lu, Dang Wu, Pin Wu   +16 more
wiley   +1 more source

Noise Effect on Linear Spectral Unmixing

Annals of GIS, 1999
Abstract Using hyperspectral reflectance data collected from six types of surface covers, we synthesized linear mixtures and used them to test the sensitivity of two linear unmixing algorithms to simulated additive noise. We found both algorithms were highly sensitive to noise. This may considerably limit their use in remote sensing.
Peng Gong, A. Zhang
openaire   +1 more source

In Vivo Microplastic Detection With Photoacoustic Imaging

Advanced Science, EarlyView.
ABSTRACT Microplastics are posing an escalating threat to both ecological systems and human health. Yet, current methods for investigating their bioaccumulation are highly invasive, requiring destructive analysis of ex vivo tissues via mass spectrometry, dye labelling, or Raman microspectroscopy.
Joseph C. Bear, Olumide Ogunlade, Jayvian Mavi, Emily J. Deniszczyc, Daolong Chen, Heeva Javaheri, Paul Beard, Mark F. Lythgoe, Daniel J. Stuckey, P. Stephen Patrick   +9 more
wiley   +1 more source

A New Fast Sparse Unmixing Algorithm Based on Adaptive Spectral Library Pruning and Nesterov Optimization

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, hyperspectral sparse unmixing (HSU) has garnered extensive research and attention due to its unique characteristic of not requiring the estimation of endmembers and their number.
Kewen Qu, Fangzhou Luo, Huiyang Wang, Wenxing Bao   +3 more
doaj   +1 more source

Genome‐Wide CRISPR Screen Identifies a microRNA Orchestrating Pleiotropic Resistance to Targeted Therapy and T Cell Immunity in Melanoma

Advanced Science, EarlyView.
A genome‐wide microRNA CRISPR screen identifies miR‐18a as a master regulator of cross‐resistance in melanoma. Loss of miR‐18a activates the AJUBA–YAP/Hippo axis to confer BRAFi resistance and enhances THBS1–CD47 interaction to impair CD8+ T cell immunity. hnRNP A1 is identified as an upstream regulator of miR‐18a processing.
Zhao Wang, Haotian Liu, Xueqi Wang, Jianjin Teng, Zihan Zheng, Jingya Zhang, Wanyi Xiao, Qian Liang, Jiaxin Li, Xiaomeng Jia, Xin Feng, Hongzhang Cui, Menghan Luo, Tielong Yang, Liuxing Wu, Ke Zhao, Weilong Yang, Mulin Jun Li, Dandan Huang, Jilong Yang   +19 more
wiley   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Spectral Unmixing With Perturbed Endmembers

IEEE Transactions on Geoscience and Remote Sensing, 2017
We consider the problem of supervised spectral unmixing with a fully-perturbed linear mixture model where the given endmembers, as well as the observations of the spectral image, are subject to perturbation due to noise, error, mismatch, etc. We calculate the Fisher information matrix and the Cramer-Rao lower bound associated with the estimation of the
openaire   +4 more sources

Hyperspectral Image Compression Optimized for Spectral Unmixing [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2016
In this paper, we present a new lossy compression method for hyperspectral images that aims to optimally compress in both spatial and spectral domains and simultaneously minimizes the effect of the compression on linear spectral unmixing performance. To achieve this, a nonnegative Tucker decomposition is applied.
Azam Karami, Rob Heylen, Paul Scheunders
openaire   +1 more source