[HTML][HTML] A review and meta-analysis of generative adversarial networks and their applications in remote sensing
Abstract Generative Adversarial Networks (GANs) are one of the most creative advances in
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …
Single-source domain expansion network for cross-scene hyperspectral image classification
Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing
attention. It is necessary to train a model only on source domain (SD) and directly …
attention. It is necessary to train a model only on source domain (SD) and directly …
The eyes of the gods: A survey of unsupervised domain adaptation methods based on remote sensing data
With the rapid development of the remote sensing monitoring and computer vision
technology, the deep learning method has made a great progress to achieve applications …
technology, the deep learning method has made a great progress to achieve applications …
Language-aware domain generalization network for cross-scene hyperspectral image classification
Text information including extensive prior knowledge about land cover classes has been
ignored in hyperspectral image (HSI) classification tasks. It is necessary to explore the …
ignored in hyperspectral image (HSI) classification tasks. It is necessary to explore the …
Cross-scene wetland mapping on hyperspectral remote sensing images using adversarial domain adaptation network
Wetlands are one of the most important ecosystems on the Earth, and using hyperspectral
remote sensing (RS) technology for fine wetland mapping is important for restoring and …
remote sensing (RS) technology for fine wetland mapping is important for restoring and …
Two-branch attention adversarial domain adaptation network for hyperspectral image classification
Recent studies have shown that deep domain adaptation (DA) techniques have good
performance on cross-domain hyperspectral image (HSI) classification problems. However …
performance on cross-domain hyperspectral image (HSI) classification problems. However …
Domain adaptation in remote sensing image classification: A survey
Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …
samples for model training. When labeled samples are unavailable or labeled samples have …
An unsupervised domain adaptation method towards multi-level features and decision boundaries for cross-scene hyperspectral image classification
C Zhao, B Qin, S Feng, W Zhu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-
scene classification, samples between source and target scenes are not drawn from the …
scene classification, samples between source and target scenes are not drawn from the …
Multilayer spectral–spatial graphs for label noisy robust hyperspectral image classification
In hyperspectral image (HSI) analysis, label information is a scarce resource and it is
unavoidably affected by human and nonhuman factors, resulting in a large amount of label …
unavoidably affected by human and nonhuman factors, resulting in a large amount of label …
Local spatial constraint and total variation for hyperspectral anomaly detection
R Feng, H Li, L Wang, Y Zhong… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Hyperspectral anomaly detection, which is aimed at locating anomaly, has received
widespread attention. In this article, a new anomaly detector, named local spatial constraint …
widespread attention. In this article, a new anomaly detector, named local spatial constraint …