[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 …

Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing

V Sagan, KT Peterson, M Maimaitijiang, P Sidike… - Earth-Science …, 2020‏ - Elsevier
Given the recent advances in remote sensing analytics, cloud computing, and machine
learning, it is imperative to evaluate capabilities of remote sensing for water quality …

Deep recurrent neural networks for hyperspectral image classification

L Mou, P Ghamisi, XX Zhu - IEEE transactions on geoscience …, 2017‏ - ieeexplore.ieee.org
In recent years, vector-based machine learning algorithms, such as random forests, support
vector machines, and 1-D convolutional neural networks, have shown promising results in …

[HTML][HTML] Hyperspectral imaging in environmental monitoring: A review of recent developments and technological advances in compact field deployable systems

MB Stuart, AJS McGonigle, JR Willmott - Sensors, 2019‏ - mdpi.com
The development and uptake of field deployable hyperspectral imaging systems within
environmental monitoring represents an exciting and innovative development that could …

Monitoring water quality using proximal remote sensing technology

X Sun, Y Zhang, K Shi, Y Zhang, N Li, W Wang… - Science of the Total …, 2022‏ - Elsevier
Accurate, high spatial and temporal resolution water quality monitoring in inland waters is
vital for environmental management. However, water quality monitoring in inland waters by …

[HTML][HTML] Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data

B Chen, X Mu, P Chen, B Wang, J Choi, H Park, S Xu… - Ecological …, 2021‏ - Elsevier
Urban rivers play an essential role in the human environment and urban development;
because of their narrow and long characteristics, challenging for general remote sensing …

Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery

C Niu, K Tan, X Jia, X Wang - Environmental pollution, 2021‏ - Elsevier
Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral
resolutions, and provides an opportunity for accurate and efficient inland water qauality …

Unsupervised spectral–spatial feature learning via deep residual Conv–Deconv network for hyperspectral image classification

L Mou, P Ghamisi, XX Zhu - IEEE Transactions on Geoscience …, 2017‏ - ieeexplore.ieee.org
Supervised approaches classify input data using a set of representative samples for each
class, known as training samples. The collection of such samples is expensive and time …

An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities

CM Lee, ML Cable, SJ Hook, RO Green… - Remote Sensing of …, 2015‏ - Elsevier
Abstract In 2007, the NASA Hyperspectral InfraRed Imager (HyspIRI) mission was
recommended in Earth Science and Applications from Space: National Imperatives for the …

A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery

H Guo, JJ Huang, B Chen, X Guo… - International Journal of …, 2021‏ - Taylor & Francis
Water-quality monitoring for small urban waterbodies by remote sensing gets to be difficult
due to the coarse spatial resolution of remote-sensing imagery. The recently launched …