Results 161 to 170 of about 1,639 (207)

Hyperspectral anomaly detection leveraging spatial attention and right-shifted spectral energy. [PDF]

PLoS One
A R, Gao Q, Zhang X, Feng W, Ali SK.
europepmc   +1 more source

Weed Species Identification Using Hyperspectral Imaging and Machine Learning. [PDF]

Plants (Basel)
Ualiyeva RM, Kaverina MM, Osipova AV, Iksat NN, Zhangazin SB.   +4 more
europepmc   +1 more source

Early Detection of Jujube Shrinkage Disease by Multi-Source Data on Multi-Task Deep Network. [PDF]

Sensors (Basel)
Pan J, Zhou L, Geng H, Zhang P, Yan F, Shi M, Si C, Chen J.   +7 more
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Quantitative analysis of tobacco blending proportions based on hyperspectral imaging and data fusion. [PDF]

Front Plant Sci
Jiang Y, Xiao Q, Huang X, Gu R, Wen J, Zhang X, Liu Y, Li L, Chen X, Yang J, He Y.   +10 more
europepmc   +1 more source

Bayesian reconstruction of rapidly scanned mid-infrared optoacoustic signals enables fast, label-free chemical microscopy. [PDF]

Sci Adv
Berger C, Kim M, Scheel-Platz L, Eigenberger A, Prantl L, Liu P, Gujrati V, Ntziachristos V, Jüstel D, Pleitez MA.   +9 more
europepmc   +1 more source

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Hyperspectral Image Denoising Using a 3-D Attention Denoising Network

IEEE Transactions on Geoscience and Remote Sensing, 2021
Hyperspectral image (HSI) denoising plays an important role in image quality improvement and related applications. Convolutional neural network (CNN)-based image denoising methods have been predominant due to advances made in the field of deep learning in recent years.
Qian Shi, Xiaopei Tang, Liangpei Zhang   +2 more
exaly   +2 more sources

Iterative Refinement Network for Hyperspectral Image Denoising

2023 IEEE International Conference on Multimedia and Expo (ICME), 2023
Hyperspectral image (HSI) denoising is an important pre-processing procedure for subsequent tasks. Learning a direct mapping from the observed noisy HSI to its clean counterpart is challenging, especially in the case of very severe noise.
Fengchao Xiong, Jun Zhou 0001, Zhuang Zhao, Yuntao Qian   +3 more
openaire   +2 more sources

Nonlocal Similarity Based Nonnegative Tucker Decomposition for Hyperspectral Image Denoising [PDF]

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018
Compared with color or grayscale images, hyperspectral images deliver more informative representation of ground objects and enhance the performance of many recognition and classification applications.
Xiao Bai, Lei Zhou, Lu Bai
exaly   +2 more sources