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[HTML][HTML] Deep learning classifiers for hyperspectral imaging: A review
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 …
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
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 …
learning, it is imperative to evaluate capabilities of remote sensing for water quality …
Deep recurrent neural networks for hyperspectral image classification
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 …
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
The development and uptake of field deployable hyperspectral imaging systems within
environmental monitoring represents an exciting and innovative development that could …
environmental monitoring represents an exciting and innovative development that could …
Monitoring water quality using proximal remote sensing technology
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 …
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 …
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
Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral
resolutions, and provides an opportunity for accurate and efficient inland water qauality …
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
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 …
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
Abstract In 2007, the NASA Hyperspectral InfraRed Imager (HyspIRI) mission was
recommended in Earth Science and Applications from Space: National Imperatives for the …
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
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 …
due to the coarse spatial resolution of remote-sensing imagery. The recently launched …