Results 121 to 130 of about 45,417 (317)

How to Learn More? Exploring Kolmogorov–Arnold Networks for Hyperspectral Image Classification

Remote Sensing
Convolutional neural networks (CNNs) and vision transformers (ViTs) have shown excellent capability in complex hyperspectral image (HSI) classification.
Ali Jamali, Swalpa Kumar Roy, Danfeng Hong, Bing Lu, Pedram Ghamisi   +4 more
doaj   +1 more source

Hyperspectral texture analysis for colon tissue biopsy classification [PDF]

, 2007
Diagnosis and cure of colon cancer can be improved by performing automated histopathological analysis of colon biopsy samples. Due to significant observational variation between pathologists in several histological features, there is a need for the ...
Rajpoot, Nasir M. (Nasir Mahmood), Qureshi, Hammad A., Rajpoot, Kashif, Masood, Khalid   +3 more
core  

Bio‐inspired nanophotonics: Structural color, chirality, and resonance metasurfaces

InfoMat, EarlyView.
A butterfly‐wing‐inspired anisotropic plasmonic flatband resonant metasurface. Insets, photo of the butterfly, Sasakia charonda, and the SEM image of its wing scale (above); the SEM image of the metasurface (below). Abstract The dazzling colors of butterfly wings and hummingbird feathers are not painted with pigments, but crafted by nature's invisible ...
Weihan Liu, Yao Liang, Din Ping Tsai
wiley   +1 more source

HyperGreen 2002. Airborne hyperspectral data from West Greenland

, 2006
For description of data set see: HyVista hyperspectral survey, central West Greenland ...
HyVista Corp. for GEUSdk
core   +1 more source

Reidite Formation From Zircon Subjected to 20, 40, and 60 GPa Shock Experiments

Journal of Raman Spectroscopy, EarlyView.
Structure disordering as a result of shock pressure: In zircon shocked from 20 to 60 GPa, the shock‐induced structural disorder increases with increasing shock pressure (gray area). Shock disorder of zircon is an integral part of its transformation to reidite: Reidite occurs after amorphous material.
Dmitry A. Zamyatin, Elizaveta Kovaleva
wiley   +1 more source

Contrastive Learning for Regression on Hyperspectral Data

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Contrastive learning has demonstrated great effectiveness in representation learning especially for image classification tasks. However, there is still a shortage in the studies targeting regression tasks, and more specifically applications on hyperspectral data.
Mohamad Dhaini, Maxime Berar, Paul Honeine, Antonin Van Exem   +3 more
openaire   +2 more sources

Development of a new spectral library classifier for airborne hyperspectral images on heterogeneous environments

, 2011
The classification of hyperspectral images on heterogeneous environments without prior knowledge about the study area is a challenging task. Finding potential pure spectral signatures or endmembers (EM) of the various surface materials within an image is
Mende, Andre, Heiden, Uta, Buchroithner, Manfred, Hoja, Danielle, Bachmann, Martin   +4 more
core  

Discriminating Chemoresistant and Chemosensitive Tubo‐Ovarian High‐Grade Serous Carcinoma Cells With Raman Microscopy

Journal of Raman Spectroscopy, EarlyView.
Confocal Raman microscopy combined with multivariate analysis distinguishes cisplatin‐resistant and ‐sensitive tubo‐ovarian high‐grade serous carcinoma cell lines. ABSTRACT Chemoresistance is a major obstacle to effective cancer treatment, particularly in tubo‐ovarian high‐grade serous carcinoma (HGSC), the most lethal gynaecological malignancy ...
Elina Harju, Parisa Movahedi, Teemu Tomberg, Sara J. Fraser‐Miller, Francesco Piga, Jukka Saarinen, Kathleen J. Sircombe, Jacopo Zini, Jukka Heikkonen, Anna Laury, Sarah Hook, Keith C. Gordon, Tapio Pahikkala, Clare J. Strachan   +13 more
wiley   +1 more source

Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model

Applied Sciences
Accurate prediction of soil properties is essential for sustainable land management and precision agriculture. This study presents an LSTM-CNN-Attention model that integrates temporal and spatial feature extraction with attention mechanisms to improve ...
Yiqiang Liu, Luming Shen, Xinghui Zhu, Yangfan Xie, Shaofang He   +4 more
doaj   +1 more source

Models of DG‐Carbon Bands in Raman Spectra: Effects of Basis Functions and Initial Values on the Uniqueness and S/N of Fits Obtained for Atmospheric Aerosol Using Automated Methods

Journal of Raman Spectroscopy, EarlyView.
Raman spectra containing D/G features are fit using combinations of basis functions and initial conditions. Fits with two Lorentzian functions converged to a single optimized fit even with variation of initial peak positions. For five‐band fits, sometimes the final fit varied with initial conditions, and constraints on peak center position were ...
David C. Doughty, Steven C. Hill
wiley   +1 more source