Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
Hyperspectral image denoising: From model-driven, data-driven, to model-data-driven
Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications.
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …
SpectralFormer: Rethinking hyperspectral image classification with transformers
Hyperspectral (HS) images are characterized by approximately contiguous spectral
information, enabling the fine identification of materials by capturing subtle spectral …
information, enabling the fine identification of materials by capturing subtle spectral …
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 …
Rotation-invariant attention network for hyperspectral image classification
X Zheng, H Sun, X Lu, W ** 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 map** is important for restoring and …
remote sensing (RS) technology for fine wetland map** is important for restoring and …
Category-specific prototype self-refinement contrastive learning for few-shot hyperspectral image classification
Deep learning (DL) has been extensively used for hyperspectral image classification (HSIC)
with significant success, but the classification of high-dimensional hyperspectral image (HSI) …
with significant success, but the classification of high-dimensional hyperspectral image (HSI) …
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 …
Hyperspectral anomaly detection with robust graph autoencoders
Anomaly detection of hyperspectral data has been gaining particular attention for its ability in
detecting targets in an unsupervised manner. Autoencoder (AE), together with its variants …
detecting targets in an unsupervised manner. Autoencoder (AE), together with its variants …
Abundance matrix correlation analysis network based on hierarchical multihead self-cross-hybrid attention for hyperspectral change detection
Hyperspectral image (HSI) change detection is a technique for detecting the changes
between the multitemporal HSIs of the same scene. Many existing change detection …
between the multitemporal HSIs of the same scene. Many existing change detection …