Results 31 to 40 of about 1,407 (163)

Pixel-Level and Global Similarity-Based Adversarial Autoencoder Network for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing is a critical task in remote sensing, enabling the decomposition of hyperspectral data into their constituent endmembers and abundances.
Wei Tao, Haiyang Zhang, Shan Zeng, Long Wang, Chaoxian Liu, Bing Li   +5 more
doaj   +1 more source

Sparse Unmixing of Hyperspectral Data [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2011
Linear spectral unmixing is a popular tool in remotely sensed hyperspectral data interpretation. It aims at estimating the fractional abundances of pure spectral signatures (also called as endmembers) in each mixed pixel collected by an imaging spectrometer.
Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio Plaza   +2 more
openaire   +1 more source

Hyperspectral Unmixing Network Accounting for Spectral Variability Based on a Modified Scaled and a Perturbed Linear Mixing Model

Remote Sensing, 2023
Spectral unmixing is one of the prime topics in hyperspectral image analysis, as images often contain multiple sources of spectra. Spectral variability is one of the key factors affecting unmixing accuracy, since spectral signatures are affected by ...
Ying Cheng, Liaoying Zhao, Shuhan Chen, Xiaorun Li   +3 more
doaj   +1 more source

High‐Content SRS Imaging Unveils Altered Cholesterol Metabolism in Ovarian Cancers Under CAR‐T Treatment

Advanced Science, EarlyView.
High‐content Stimulated Raman Scattering (SRS) Imaging reveals that ovarian cancer cells surviving Chimeric Antigen Receptor (CAR) ‐T cell challenge exhibit increased cholesterol esterification. Pharmacological inhibition of this pathway with Avasimibe significantly enhances CAR‐T induced killing of ovarian cancer cells by reducing cancer cell ...
Chinmayee V. Prabhu Dessai, Zhuoying Huang, Haonan Lin, Guangrui Ding, Hongjian He, Menna Siddiqui, Meng Zhang, Wilson Wong, Ji‐Xin Cheng   +8 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

Adaptive Graph Regularized Multilayer Nonnegative Matrix Factorization for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Hyperspectral unmixing is an important technique for remote sensing image analysis. Among various unmixing techniques, nonnegative matrix factorization (NMF) shows unique advantage in providing a unified solution with well physical interpretation.
Lei Tong, Jun Zhou, Bin Qian, Jing Yu, Chuangbai Xiao   +4 more
doaj   +1 more source

HYPERSPECTRAL IMAGE RESOLUTION ENHANCEMENT BASED ON SPECTRAL UNMIXING AND INFORMATION FUSION [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012
Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels. Spectral unmixing methods can be
J. Bieniarz, D. Cerra, J. Avbelj, P. Reinartz, R. Müller   +4 more
doaj   +1 more source

Robust Hyperspectral Unmixing With Correntropy-Based Metric

IEEE Transactions on Image Processing, 2015
Hyperspectral unmixing is one of the crucial steps for many hyperspectral applications. The problem of hyperspectral unmixing has proven to be a difficult task in unsupervised work settings where the endmembers and abundances are both unknown. What is more, this task becomes more challenging in the case that the spectral bands are degraded with noise ...
Wang, Ying, Pan, Chunhong, Xiang, Shiming, Zhu, Feiyun   +3 more
openaire   +3 more sources

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

Deep Learning Integration in Optical Microscopy: Advancements and Applications

Microscopy Research and Technique, EarlyView.
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 ...
Pottumarthy Venkata Lahari, Sagnika Dutta, H. Deeksha, Samreen A. Patel, Budheswar Dehury, Nirmal Mazumder   +5 more
wiley   +1 more source