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 …
CoSpace: Common subspace learning from hyperspectral-multispectral correspondences
With a large amount of open satellite multispectral (MS) imagery (eg, Sentinel-2 and Landsat-
8), considerable attention has been paid to global MS land cover classification. However, its …
8), considerable attention has been paid to global MS land cover classification. However, its …
Multiscale representation learning for image classification: A survey
Feature representation has been widely used and developed recently. Multiscale features
have led to remarkable breakthroughs for representation learning process in many computer …
have led to remarkable breakthroughs for representation learning process in many computer …
Hyperspectral image classification based on domain adversarial broad adaptation network
For hyperspectral image (HSI) classification tasks, obtaining sufficient labeled samples is
usually difficult, time-consuming, and expensive. To address the aforementioned issue, by …
usually difficult, time-consuming, and expensive. To address the aforementioned issue, by …
Hyperspectral image classification based on domain adaptation broad learning
Hyperspectral images (HSI) are widely applied in numerous fields for their rich spatial and
spectral information. However, in these applications, we always face the situation that the …
spectral information. However, in these applications, we always face the situation that the …
Gradient feature-oriented 3-D domain adaptation for hyperspectral image classification
Domain adaptation, which cleverly applies the classifier learned from the source domain
with sufficient labeled samples to the target domain with limited labeled samples, provides a …
with sufficient labeled samples to the target domain with limited labeled samples, provides a …
Hyperspectral target detection with macro-micro feature extracted by 3-D residual autoencoder
Unsupervised autoencoders (AEs) have been demonstrated effectively to achieve robust
performance in hyperspectral feature extraction. However, one-dimension inputs of AE …
performance in hyperspectral feature extraction. However, one-dimension inputs of AE …
Soft instance-level domain adaptation with virtual classifier for unsupervised hyperspectral image classification
Y Cheng, Y Chen, Y Kong… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Adversarial learning-based unsupervised hyperspectral image (HSI) classification methods
usually adapt probability distributions by minimizing the statistical distance between similar …
usually adapt probability distributions by minimizing the statistical distance between similar …
Sub-pixel dispersion model for coded aperture snapshot spectral imaging
Coded aperture snapshot spectral imaging (CASSI) aims to reconstruct three-dimensional
spatial-spectral images from a single snapshot measurement. Traditional CASSI systems …
spatial-spectral images from a single snapshot measurement. Traditional CASSI systems …
An iterative training sample updating approach for domain adaptation in hyperspectral image classification
S Zhong, Y Zhang - IEEE Geoscience and Remote Sensing …, 2020 - ieeexplore.ieee.org
Acquiring training samples in remote sensing images is always expensive and time-
consuming. As a consequence, it would be preferable if one domain without training …
consuming. As a consequence, it would be preferable if one domain without training …