Land-use/land-cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery

Q Zhu, X Guo, W Deng, S Shi, Q Guan, Y Zhong… - ISPRS Journal of …, 2022 - Elsevier
Due to the abundant features of high spatial resolution (HSR) remote sensing images,
change detection of these images is crucial to understanding the land-use and land-cover …

Hyperspectral image classification with multi-attention transformer and adaptive superpixel segmentation-based active learning

C Zhao, B Qin, S Feng, W Zhu, W Sun… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Deep learning (DL) based methods represented by convolutional neural networks (CNNs)
are widely used in hyperspectral image classification (HSIC). Some of these methods have …

NSCKL: Normalized spectral clustering with kernel-based learning for semisupervised hyperspectral image classification

Y Su, L Gao, M Jiang, A Plaza, X Sun… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Spatial–spectral classification (SSC) has become a trend for hyperspectral image (HSI)
classification. However, most SSC methods mainly consider local information, so that some …

IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification

Y He, B Tu, P Jiang, B Liu, J Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Hyperspectral image (HSI) classification has garnered substantial attention in remote
sensing fields. Recent Mamba architectures built upon the Selective State Space Models …

Spectral–spatial masked transformer with supervised and contrastive learning for hyperspectral image classification

L Huang, Y Chen, X He - IEEE Transactions on Geoscience …, 2023 - ieeexplore.ieee.org
Recently, due to the powerful capability at modeling the long-range relationships,
Transformer-based methods have been widely explored in many research areas, including …

From center to surrounding: An interactive learning framework for hyperspectral image classification

J Yang, B Du, L Zhang - ISPRS Journal of Photogrammetry and Remote …, 2023 - Elsevier
Owing to rich spectral and spatial information, hyperspectral image (HSI) can be utilized for
finely classifying different land covers. With the emergence of deep learning techniques …

ACGT-Net: Adaptive cuckoo refinement-based graph transfer network for hyperspectral image classification

Y Su, J Chen, L Gao, A Plaza, M Jiang… - … on Geoscience and …, 2023 - ieeexplore.ieee.org
Deep learning (DL) has brought many new trends for hyperspectral image classification
(HIC). Graph neural networks (GNNs) are models that fuse DL and structured data. Although …

A lightweight transformer network for hyperspectral image classification

X Zhang, Y Su, L Gao, L Bruzzone… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Transformer is a powerful tool for capturing long-range dependencies and has shown
impressive performance in hyperspectral image (HSI) classification. However, such power …

Universal domain adaptation for remote sensing image scene classification

Q Xu, Y Shi, X Yuan, XX Zhu - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The domain adaptation (DA) approaches available to date are usually not well suited for
practical DA scenarios of remote sensing image classification since these methods (such as …

A 3-d-swin transformer-based hierarchical contrastive learning method for hyperspectral image classification

X Huang, M Dong, J Li, X Guo - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Deep convolutional neural networks have been dominating in the field of hyperspectral
image (HSI) classification. However, single convolutional kernel can limit the receptive field …