A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications

A Khan, AD Vibhute, S Mali, CH Patil - Ecological Informatics, 2022 - Elsevier
The globe's population is increasing day by day, which causes the severe problem of
organic food for everyone. Farmers are becoming progressively conscious of the need to …

[HTML][HTML] Deep learning classifiers for hyperspectral imaging: A review

ME Paoletti, JM Haut, J Plaza, A Plaza - ISPRS Journal of Photogrammetry …, 2019 - Elsevier
Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Interpretable hyperspectral artificial intelligence: When nonconvex modeling meets hyperspectral remote sensing

D Hong, W He, N Yokoya, J Yao, L Gao… - … and Remote Sensing …, 2021 - ieeexplore.ieee.org
Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …

Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review

S Peyghambari, Y Zhang - Journal of Applied Remote Sensing, 2021 - spiedigitallibrary.org
Hyperspectral imaging has been used in a variety of geological applications since its advent
in the 1970s. In the last few decades, different techniques have been developed by …

Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art

P Ghamisi, N Yokoya, J Li, W Liao, S Liu… - … and Remote Sensing …, 2017 - ieeexplore.ieee.org
Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …

Toward integrated large-scale environmental monitoring using WSN/UAV/Crowdsensing: A review of applications, signal processing, and future perspectives

A Fascista - Sensors, 2022 - mdpi.com
Fighting Earth's degradation and safeguarding the environment are subjects of topical
interest and sources of hot debate in today's society. According to the United Nations, there …

Towards the spectral restoration of shadowed areas in hyperspectral images based on nonlinear unmixing

G Zhang, D Cerra, R Muller - 2019 10th Workshop on …, 2019 - ieeexplore.ieee.org
This work proposes a new shadow restoration method for hyperspectral images based on
nonlinear unmixing. A physical model is introduced to estimate the shadowed spectrum from …

Hyperspectral anomaly detection with attribute and edge-preserving filters

X Kang, X Zhang, S Li, K Li, J Li… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
A novel method for anomaly detection in hyperspectral images is proposed. The method is
based on two ideas. First, compared with the surrounding background, objects with …

DAEN: Deep autoencoder networks for hyperspectral unmixing

Y Su, J Li, A Plaza, A Marinoni, P Gamba… - … on Geoscience and …, 2019 - ieeexplore.ieee.org
Spectral unmixing is a technique for remotely sensed image interpretation that expresses
each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and …

CyCU-Net: Cycle-consistency unmixing network by learning cascaded autoencoders

L Gao, Z Han, D Hong, B Zhang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In recent years, deep learning (DL) has attracted increasing attention in hyperspectral
unmixing (HU) applications due to its powerful learning and data fitting ability. The …