Results 221 to 230 of about 109,457 (327)

A new band selection approach integrated with physical reflectance autoencoders and albedo recovery for hyperspectral image classification. [PDF]

Sci Rep
Sangeetha V, Agilandeeswari L.
europepmc   +1 more source

High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)

, 2016
Glynn Hulley, Riley Duren, F. M. Hopkins, S. J. Hook, N. Vance, P. Guillevic, W. R. Johnson, B. T. Eng, J. M. Mihaly, Vladan Jovanović, S. L. Chazanoff, Z. K. Staniszewski, Le Kuai, John R. Worden, Christian Frankenberg, G. Rivera, A. D. Aubrey, Charles E. Miller, N. K. Malakar, Juan Manuel Sánchez, K. T. Holmes   +20 more
openalex   +1 more source

Viable but Nonculturable State, a Survival Strategy for Salmonella in Aquatic Environments

Water and Environment Journal, EarlyView.
Transition of Salmonella from a culturable state to a viable but nonculturable state under various environmental stresses and current methods for detecting viable Salmonella in environmental samples. ABSTRACT In the relentless battle for survival, Salmonella has evolved mechanisms to withstand harsh conditions such as extreme temperatures, fluctuating ...
Sanelisiwe Thinasonke Duze, Musa Marimani, Katlego Mamabolo, Mrudula Patel   +3 more
wiley   +1 more source

Multiscale superpixel depth feature extraction for hyperspectral image classification. [PDF]

Sci Rep
Yan Q, Zhang S, Chen X, Zheng Z.
europepmc   +1 more source

Robustness of high‐throughput prediction of leaf ecophysiological traits using near infrared spectroscopy and poro‐fluorometry

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Water scarcity is a major threat to crop production and quality. Improving drought tolerance through variety selection requires a deeper understanding of plant ecophysiological responses, but large‐scale phenotyping remains a bottleneck. This study assessed the potential of high‐throughput tools (spectroscopy and poro‐fluorometry) to predict ...
Eva Coindre, Romain Boulord, Laurine Chir, Virgilio Freitas, Maxime Ryckewaert, Thomas Laisné, Virginie Bouckenooghe, Maëlle Lis, Llorenç Cabrera‐Bosquet, Agnès Doligez, Thierry Simonneau, Benoît Pallas, Aude Coupel‐Ledru, Vincent Segura   +13 more
wiley   +1 more source

Hyperspectral Image Change Detection Method Based on the Balanced Metric. [PDF]

Sensors (Basel)
Liang X, Li X, Wang Q, Qian J, Wang Y.
europepmc   +1 more source

Feature extraction of hyperspectral images based on SVM optimization of 2D-EMD and GWO [PDF]

Jian Tang, Dan Li, Lei Zhang, Xiangtong Nan, Xin Li, Luo Dan, Qianliang Xiao   +6 more
openalex   +1 more source

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source

ARM-Net: A Tri-Phase Integrated Network for Hyperspectral Image Compression. [PDF]

Sensors (Basel)
Fang Q, Wang Z, Wang J, Zhang L.
europepmc   +1 more source

Ultrafast hyperspectral nanomotion imaging of ballistic and Brownian motion in metamaterial nanostructures [PDF]

, 2020
Tongjun Liu, Jun‐Yu Ou, Nikolay I. Zheludev, Kevin F. MacDonald   +3 more
openalex