[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 …
Hypersectral imaging for military and security applications: Combining myriad processing and sensing techniques
M Shimoni, R Haelterman… - IEEE Geoscience and …, 2019 - ieeexplore.ieee.org
Collecting airborne and spaceborne intelligence, surveillance, and reconnaissance (ISR)
information is mandatory for addressing the defense challenges posed in the 21st century. A …
information is mandatory for addressing the defense challenges posed in the 21st century. A …
Advances in spectral-spatial classification of hyperspectral images
Recent advances in spectral-spatial classification of hyperspectral images are presented in
this paper. Several techniques are investigated for combining both spatial and spectral …
this paper. Several techniques are investigated for combining both spatial and spectral …
Single-shot hyperspectral-depth imaging with learned diffractive optics
Imaging depth and spectrum have been extensively studied in isolation from each other for
decades. Recently, hyperspectral-depth (HS-D) imaging emerges to capture both …
decades. Recently, hyperspectral-depth (HS-D) imaging emerges to capture both …
Advances in hyperspectral remote sensing of vegetation and agricultural crops
PS Thenkabail, JG Lyon, A Huete - … , Sensor Systems, Spectral …, 2018 - taylorfrancis.com
Hyperspectral data (Table 1) is acquired as continuous narrowbands (eg, each band with 1
to 10 nanometer or nm bandwidths) over a range of electromagnetic spectrum (eg, 400 …
to 10 nanometer or nm bandwidths) over a range of electromagnetic spectrum (eg, 400 …
Multiple kernel learning for hyperspectral image classification: A review
With the rapid development of spectral imaging techniques, classification of hyperspectral
images (HSIs) has attracted great attention in various applications such as land survey and …
images (HSIs) has attracted great attention in various applications such as land survey and …
Landslide detection of hyperspectral remote sensing data based on deep learning with constrains
C Ye, Y Li, P Cui, L Liang, S Pirasteh… - IEEE Journal of …, 2019 - ieeexplore.ieee.org
Detecting and monitoring landslides are hot topics in remote sensing community, particularly
with the development of remote sensing technologies and the significant progress of …
with the development of remote sensing technologies and the significant progress of …
Transformer-based masked autoencoder with contrastive loss for hyperspectral image classification
X Cao, H Lin, S Guo, T Xiong… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In recent years, in order to solve the problem of lacking accurately labeled hyperspectral
image data, self-supervised learning has become an effective method for hyperspectral …
image data, self-supervised learning has become an effective method for hyperspectral …
Recent advances in counterfeit art, document, photo, hologram, and currency detection using hyperspectral imaging
Forgery and tampering continue to provide unnecessary economic burdens. Although new
anti-forgery and counterfeiting technologies arise, they inadvertently lead to the …
anti-forgery and counterfeiting technologies arise, they inadvertently lead to the …
Multimodal hyperspectral remote sensing: An overview and perspective
Since the advent of hyperspectral remote sensing in the 1980s, it has made important
achievements in aerospace and aviation field and been applied in many fields …
achievements in aerospace and aviation field and been applied in many fields …