Results 101 to 110 of about 1,349 (217)

Hyperspectral clustering using atrous spatial-spectral convolutional network [PDF]

, 2022
: Hyperspectral imaging is an important technology in the field of geosciences and remote sensing. However, the high-dimensional nature of hyperspectral images (HSIs) together with the limited availability of training/labeled samples challenge an ...
Rafiezadeh Shahi, Kasra, Ghamisi, Pedram, Scheunders, Paul, Rasti, Behnood, Gloaguen, Richard   +4 more
core  

Assessing Moisture Content of Coolstored Blueberries During Water Loss Scenarios Using Hyperspectral Imaging

New Zealand Journal of Crop and Horticultural Science, Volume 54, Issue 2, June 2026.
Water loss is a key factor affecting the postharvest quality and shelf life of blueberries, and storage conditions (humidity and time) play an important role in regulating water retention capacity of stored berries. This study aims to explore the variation of moisture content (MC) in blueberries under different storage humidity and storage time ...
RunKai Wang, Craig McGill, Eduardo Sandoval, Mo Li   +3 more
wiley   +1 more source

Multimodal Assessment of Kidney Quality During 24‐h of Normothermic Machine Perfusion

Artificial Organs, Volume 50, Issue 6, Page 851-865, June 2026.
Normothermic machine perfusion (NMP) has emerged as a promising tool for assessing kidney quality prior to transplantation; however, reliable biomarkers remain to be established. In this study, thirteen porcine kidneys were perfused for 24 h using an autologous leukocyte‐filtered whole blood‐based perfusate applying urine recirculation.
Marlene Pühringer, Magdalena Kött, Andras T. Meszaros, Michael J. Blumer, Bettina Zelger, Stefan Redl, Martin Hermann, Thomas Resch, Simon Mathis, Gabriel Putzer, Judith Martini, Christina Bogensperger, Franka Messner, Julia Hofmann, Margot Fodor, Andrea Griesmacher, Annemarie Weissenbacher, Theresa Hautz, Gert Mayer, Hannes Neuwirt, Gerald Brandacher, Silvia Oberparleiter, Stefan Schneeberger   +22 more
wiley   +1 more source

Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset

Scientific Data
Abstract The global challenge of sustainable recycling demands automated, fast, and accurate material detection systems that act as a bedrock for a circular economy. Integrating front-tier technologies into advanced recycling systems democratizes access to AI-driven sustainability, and transforms waste analysis ...
Arbash, E., Afifi, A. J. M., Belahsen, Y., Fuchs, M., Ghamisi, P., Scheunders, P., Gloaguen, R.   +6 more
openaire   +6 more sources

Learning spectral and spatial features based on generative adversarial network for hyperspectral image super-resolution [PDF]

, 2019
Super-resolution (SR) of hyperspectral images (HSIs) aims to enhance the spatial/spectral resolution of hyperspectral imagery and the super-resolved results will benefit many remote sensing applications.
Lei Zhang (38117), Xu Li (23864), Ruituo Jiang (17924348), Lixin Li (568791), Ang Gao (1819549), Shigang Yue (17170645), Hongying Meng (17163403)   +6 more
core   +2 more sources

Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection [PDF]

, 2017
© 1980-2012 IEEE. With the high spectral resolution, hyperspectral images (HSIs) provide great potential for target detection, which is playing an increasingly important role in HSI processing.
Liu, T, Du, B, Zhang, L, Zhang, Y
core   +1 more source

Multiorder Graph Convolutional Network With Channel Attention for Hyperspectral Change Detection

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral change detection (CD) aims to obtain the change information of objects in the multitemporal hyperspectral images (HSIs). Recently, with the advantages in fully extracting the image features of irregular areas, the graph convolutional ...
Yuxiang Zhang, Rui Miao, Yanni Dong, Bo Du   +3 more
doaj   +1 more source

Landmark-based large-scale sparse subspace clustering method for hyperspectral images [PDF]

, 2019
Sparse subspace clustering (SSC) has achieved the state-of-the-art performance in the clustering of hyperspectral images (HSIs). However, the high computational complexity and sensitivity to noise limit its clustering performance.
Zhang, Hongyan, Shaoguang Huang, Aleksandra Pizurica, Huang, Shaoguang, Pizurica, Aleksandra, Hongyan Zhang   +5 more
core   +2 more sources

Hyperspectral Anomaly Detection via Merging Total Variation Into Low-Rank Representation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Anomaly detection (AD) aiming to locate targets distinct from the surrounding background spectra remains a challenging task in hyperspectral applications.
Linwei Li, Ziyu Wu, Bin Wang
doaj   +1 more source

SRS: 3D Mapping and Hyperspectral Imaging (HSI) v1

We have developed a protocol for utilizing stimulated Raman scattering (SRS) to acquirelabel-free measurements of specific macromolecules. When performing SRS imagingwe can acquire total protein, total lipid, unsaturated lipid, and saturatedlipid concentration from distinct Raman shift wavenumbers.
Jorge Villazon, Zhi Li, Yajuan Li, Hongje Jang, Anthony Fung, Lingyan Shi, Sanjay Jain   +6 more
openaire   +1 more source