[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 …
[HTML][HTML] Deep learning for urban land use category classification: A review and experimental assessment
Mapping the distribution, pattern, and composition of urban land use categories plays a
valuable role in understanding urban environmental dynamics and facilitating sustainable …
valuable role in understanding urban environmental dynamics and facilitating sustainable …
A hybrid deep-learning model for fault diagnosis of rolling bearings
Detection accuracy of bearing faults is crucial in saving economic loss for industrial
applications. Deep learning is capable of producing high accuracy for bearing fault …
applications. Deep learning is capable of producing high accuracy for bearing fault …
Hyperspectral image classification: Potentials, challenges, and future directions
Recent imaging science and technology discoveries have considered hyperspectral
imagery and remote sensing. The current intelligent technologies, such as support vector …
imagery and remote sensing. The current intelligent technologies, such as support vector …
Very high resolution remote sensing imagery classification using a fusion of random forest and deep learning technique—Subtropical area for example
Recently, convolutional neural networks (CNNs) showed excellent performance in many
tasks, such as computer vision and remote sensing semantic segmentation. Especially, the …
tasks, such as computer vision and remote sensing semantic segmentation. Especially, the …
Potential of hybrid CNN-RF model for early crop mapping with limited input data
When sufficient time-series images and training data are unavailable for crop classification,
features extracted from convolutional neural network (CNN)-based representative learning …
features extracted from convolutional neural network (CNN)-based representative learning …
Conventional to deep ensemble methods for hyperspectral image classification: A comprehensive survey
Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral
imaging (HSI) has been widely used in a wide range of real-world application areas due to …
imaging (HSI) has been widely used in a wide range of real-world application areas due to …
A new convolutional neural network with random forest method for hydrogen sensor fault diagnosis
Y Sun, H Zhang, T Zhao, Z Zou, B Shen, L Yang - IEEE Access, 2020 - ieeexplore.ieee.org
Hydrogen is considered to be a hazardous substance. Hydrogen sensors can be used to
detect the concentration of hydrogen and provide an ideal monitoring means for the safe use …
detect the concentration of hydrogen and provide an ideal monitoring means for the safe use …
Traffic density classification using sound datasets: an empirical study on traffic flow at asymmetric roads
Recently, with the rapid growth of Deep Learning models for solving complicated
classification problems, urban sound classification techniques have been attracted more …
classification problems, urban sound classification techniques have been attracted more …
Deep learning-based prediction of traffic accidents risk for Internet of vehicles
H Zhao, X Li, H Cheng, J Zhang, Q Wang… - China …, 2022 - ieeexplore.ieee.org
With the increasing number of vehicles, traffic accidents pose a great threat to human lives.
Hence, aiming at reducing the occurrence of traffic accidents, this paper proposes an …
Hence, aiming at reducing the occurrence of traffic accidents, this paper proposes an …