A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications
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 …
organic food for everyone. Farmers are becoming progressively conscious of the need to …
Deep feature fusion via two-stream convolutional neural network for hyperspectral image classification
X Li, M Ding, A Pižurica - IEEE Transactions on Geoscience …, 2019 - ieeexplore.ieee.org
The representation power of convolutional neural network (CNN) models for hyperspectral
image (HSI) analysis is in practice limited by the available amount of the labeled samples …
image (HSI) analysis is in practice limited by the available amount of the labeled samples …
Hybrid 3D/2D complete inception module and convolutional neural network for hyperspectral remote sensing image classification
Classification in hyperspectral remote sensing images (HRSIs) is a challenging process in
image analysis and one of the most popular topics. In recent years, many methods have …
image analysis and one of the most popular topics. In recent years, many methods have …
[HTML][HTML] A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification
In comparison with conventional machine learning algorithms, deep learning can effectively
express the deep features of remote sensing images. Considering the rich spectral and …
express the deep features of remote sensing images. Considering the rich spectral and …
[HTML][HTML] Fast super-resolution of 20 m Sentinel-2 bands using convolutional neural networks
Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of
information for the entire remote sensing community, thanks to their unprecedented …
information for the entire remote sensing community, thanks to their unprecedented …
[HTML][HTML] Uncovering the hidden: Leveraging sub-pixel spectral diversity to estimate plant diversity from space
C Rossi, H Gholizadeh - Remote Sensing of Environment, 2023 - Elsevier
Remotely sensed spectral diversity has emerged as a promising proxy for plant diversity.
However, spectral diversity approaches relate image spectra to plant community diversity by …
However, spectral diversity approaches relate image spectra to plant community diversity by …
Land use/land cover (LULC) classification using hyperspectral images: a review
C Lou, MAA Al-qaness, D AL-Alimi… - Geo-spatial …, 2024 - Taylor & Francis
In the rapidly evolving realm of remote sensing technology, the classification of
Hyperspectral Images (HSIs) is a pivotal yet formidable task. Hindered by inherent …
Hyperspectral Images (HSIs) is a pivotal yet formidable task. Hindered by inherent …
[HTML][HTML] A detail-preserving cross-scale learning strategy for CNN-based pansharpening
The fusion of a single panchromatic (PAN) band with a lower resolution multispectral (MS)
image to raise the MS resolution to that of the PAN is known as pansharpening. In the last …
image to raise the MS resolution to that of the PAN is known as pansharpening. In the last …
Challenging the link between functional and spectral diversity with radiative transfer modeling and data
In a context of accelerated human-induced biodiversity loss, remote sensing (RS) is
emerging as a promising tool to map plant biodiversity from space. Proposed approaches …
emerging as a promising tool to map plant biodiversity from space. Proposed approaches …
Style transformation-based spatial–spectral feature learning for unsupervised change detection
G Liu, Y Yuan, Y Zhang, Y Dong… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Due to the inconsistent imaging environment, the styles of multitemporal multispectral
images (MSIs) are quite different, such as image brightness and transparency. For …
images (MSIs) are quite different, such as image brightness and transparency. For …