[PDF][PDF] Deep unsupervised domain adaptation: A review of recent advances and perspectives
Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …
domains, partly because of its ability to learn from data and achieve impressive performance …
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 …
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 …
A fast and compact 3-D CNN for hyperspectral image classification
Hyperspectral images (HSIs) are used in a large number of real-world applications. HSI
classification (HSIC) is a challenging task due to high interclass similarity, high intraclass …
classification (HSIC) is a challenging task due to high interclass similarity, high intraclass …
Dimensionality reduction with enhanced hybrid-graph discriminant learning for hyperspectral image classification
Dimensionality reduction (DR) is an important way of improving the classification accuracy of
a hyperspectral image (HSI). Graph learning, which can effectively reveal the intrinsic …
a hyperspectral image (HSI). Graph learning, which can effectively reveal the intrinsic …
Global rectification and decoupled registration for few-shot segmentation in remote sensing imagery
Few-shot segmentation (FSS), which aims to determine specific objects in the query image
given only a handful of densely labeled samples, has received extensive academic attention …
given only a handful of densely labeled samples, has received extensive academic attention …
Coarse-to-fine domain adaptive semantic segmentation with photometric alignment and category-center regularization
Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet
promising task relieving the need for laborious annotation works. However, the domain …
promising task relieving the need for laborious annotation works. However, the domain …
Error-tolerant deep learning for remote sensing image scene classification
Due to its various application potentials, the remote sensing image scene classification
(RSSC) has attracted a broad range of interests. While the deep convolutional neural …
(RSSC) has attracted a broad range of interests. While the deep convolutional neural …