A research review on deep learning combined with hyperspectral Imaging in multiscale agricultural sensing
Efficient and automated data acquisition techniques, as well as intelligent and accurate data
processing and analysis techniques, are essential for the advancement of precision …
processing and analysis techniques, are essential for the advancement of precision …
Methods and datasets on semantic segmentation for Unmanned Aerial Vehicle remote sensing images: A review
Abstract Unmanned Aerial Vehicle (UAV) has seen a dramatic rise in popularity for remote-
sensing image acquisition and analysis in recent years. It has brought promising results in …
sensing image acquisition and analysis in recent years. It has brought promising results in …
Hyperspectral image classification—Traditional to deep models: A survey for future prospects
Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …
because it benefits from the detailed spectral information contained in each pixel. Notably …
Research progress on few-shot learning for remote sensing image interpretation
The rapid development of deep learning brings effective solutions for remote sensing image
interpretation. Training deep neural network models usually require a large number of …
interpretation. Training deep neural network models usually require a large number of …
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 …
An extensive review of hyperspectral image classification and prediction: techniques and challenges
Abstract Hyperspectral Image Processing (HSIP) is an essential technique in remote
sensing. Currently, extensive research is carried out in hyperspectral image processing …
sensing. Currently, extensive research is carried out in hyperspectral image processing …
[HTML][HTML] Deep relation network for hyperspectral image few-shot classification
K Gao, B Liu, X Yu, J Qin, P Zhang, X Tan - Remote Sensing, 2020 - mdpi.com
Deep learning has achieved great success in hyperspectral image classification. However,
when processing new hyperspectral images, the existing deep learning models must be …
when processing new hyperspectral images, the existing deep learning models must be …
Pseudolabel-based unreliable sample learning for semi-supervised hyperspectral image classification
H Yao, R Chen, W Chen, H Sun… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Recently, pseudolabel-based deep learning methods have shown excellent performance in
semi-supervised hyperspectral image (HSI) classification. These methods usually select …
semi-supervised hyperspectral image (HSI) classification. These methods usually select …
Scattering model guided adversarial examples for SAR target recognition: Attack and defense
Deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target
recognition (ATR) systems have been shown to be highly vulnerable to adversarial …
recognition (ATR) systems have been shown to be highly vulnerable to adversarial …
Transformer-based masked autoencoder with contrastive loss for hyperspectral image classification
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 …