GraphGST: Graph generative structure-aware transformer for hyperspectral image classification
Transformer holds significance in deep learning (DL) research. Node embedding (NE) and
positional encoding (PE) are usually two indispensable components in a Transformer. The …
positional encoding (PE) are usually two indispensable components in a Transformer. The …
Development of Geographic Information System Architecture Feature Analysis and Evolution Trend Research
X Li, J Yue, S Wang, Y Luo, C Su, J Zhou, D Xu, H Lu - Sustainability, 2023 - mdpi.com
A geographic information system (GIS) is a technical system which is supported by computer
software and hardware systems. It focuses on the geographical information related to the …
software and hardware systems. It focuses on the geographical information related to the …
Hypersinet: A synergetic interaction network combined with convolution and transformer for hyperspectral image classification
Q Yu, W Wei, D Li, Z Pan, C Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In hyperspectral images (HSIs), both local and nonlocal features play crucial roles in
classification tasks. Vision transformers (VITs) can extract nonlocal features through …
classification tasks. Vision transformers (VITs) can extract nonlocal features through …
Multi-level feature extraction networks for hyperspectral image classification
S Fang, X Li, S Tian, W Chen, E Zhang - Remote Sensing, 2024 - mdpi.com
Hyperspectral image (HSI) classification plays a key role in the field of earth observation
missions. Recently, transformer-based approaches have been widely used for HSI …
missions. Recently, transformer-based approaches have been widely used for HSI …
Local-global gated convolutional neural network for hyperspectral image classification
How to learn the most valuable and useful features in convolutional neural networks (CNNs)
is the key for accurate hyperspectral image classification (HSIC). Focused on this issue, we …
is the key for accurate hyperspectral image classification (HSIC). Focused on this issue, we …
SFFNet: Staged Feature Fusion Network of Connecting Convolutional Neural Networks and Graph Convolutional Neural Networks for Hyperspectral Image …
The immense representation power of deep learning frameworks has kept them in the
spotlight in hyperspectral image (HSI) classification. Graph Convolutional Neural Networks …
spotlight in hyperspectral image (HSI) classification. Graph Convolutional Neural Networks …
Multi-scale Graph Clustering Network
X Li, W Wu, B Zhang, X Peng - Information Sciences, 2024 - Elsevier
Deep graph clustering, a fundamental yet formidable task in data analysis, aims to partition
samples belonging to the same category into their respective clusters. Recently, significant …
samples belonging to the same category into their respective clusters. Recently, significant …
HyperGCN–a multi-layer multi-exit graph neural network to enhance hyperspectral image classification
H Rahmath P, K Chaurasia, A Gupta… - International Journal of …, 2024 - Taylor & Francis
Graph neural networks (GNNs) have recently garnered significant attention due to their
exceptional performance across various applications, including hyperspectral (HS) image …
exceptional performance across various applications, including hyperspectral (HS) image …