Land-use/land-cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery
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 …
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
Deep learning (DL) based methods represented by convolutional neural networks (CNNs)
are widely used in hyperspectral image classification (HSIC). Some of these methods have …
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
Spatial–spectral classification (SSC) has become a trend for hyperspectral image (HSI)
classification. However, most SSC methods mainly consider local information, so that some …
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 …
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 …
Transformer-based methods have been widely explored in many research areas, including …
From center to surrounding: An interactive learning framework for hyperspectral image classification
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 …
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
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 …
(HIC). Graph neural networks (GNNs) are models that fuse DL and structured data. Although …
A lightweight transformer network for hyperspectral image classification
Transformer is a powerful tool for capturing long-range dependencies and has shown
impressive performance in hyperspectral image (HSI) classification. However, such power …
impressive performance in hyperspectral image (HSI) classification. However, such power …
Universal domain adaptation for remote sensing image scene classification
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 …
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
Deep convolutional neural networks have been dominating in the field of hyperspectral
image (HSI) classification. However, single convolutional kernel can limit the receptive field …
image (HSI) classification. However, single convolutional kernel can limit the receptive field …