Results 211 to 220 of about 57,062 (333)
HSTI is a powerful tool for detecting FM within EFR. The study compares spectral ranges, light intensity, and classification models, showing that FM is most effectively identified with 110 W light intensity in the VNIR system using MLP. Pseudo‐color maps enable precise localization and contouring of FM in EFR.
Peipei Gao +8 more
wiley +1 more source
Spectral-Spatial Attention Transformer with Dense Connection for Hyperspectral Image Classification. [PDF]
Dang L, Weng L, Dong W, Li S, Hou Y.
europepmc +1 more source
Laser‐induced breakdown spectroscopy (LIBS) emerges as a transformative tool for rapid, on‐site pesticide residue detection in food, offering real‐time screening with minimal sample preparation and eliminating solvent use. Although LIBS demonstrates detection limits (0.009–6.9 mg/kg) suitable for regulatory screening and aligns with green chemistry ...
Mohammad Mehdizadeh +5 more
wiley +1 more source
A Dense Pyramidal Residual Network with a Tandem Spectral-Spatial Attention Mechanism for Hyperspectral Image Classification. [PDF]
Guan Y, Li Z, Wang N.
europepmc +1 more source
A new hyperspectral image classification method based on spatial-spectral features. [PDF]
Shenming Q, Xiang L, Zhihua G.
europepmc +1 more source
An object-based approach to quantity and quality assessment of heathland habitats in the framework of natura 2000 using hyperspectral airborne ahs images [PDF]
Bertels, L. +8 more
core +1 more source
In this study, to address the problem of high cost and strong subjectivity of artificial grading of Agaricus bisporus, we propose a fast real‐time video‐based machine vision grading system, which achieves high‐speed grading of 1066.67 mushrooms per minute with an accuracy of 97.87% by lightweighting the YOLOv5 model and optimizing the balance between ...
Qiyang Shui +5 more
wiley +1 more source
Adaptive pixel attention network for hyperspectral image classification. [PDF]
Zhao Y +5 more
europepmc +1 more source
CAEVT: Convolutional Autoencoder Meets Lightweight Vision Transformer for Hyperspectral Image Classification. [PDF]
Zhang Z, Li T, Tang X, Hu X, Peng Y.
europepmc +1 more source

