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Diffusion models meet remote sensing: Principles, methods, and perspectives
As a newly emerging advance in deep generative models, diffusion models have achieved
state-of-the-art results in many fields, including computer vision, natural language …
state-of-the-art results in many fields, including computer vision, natural language …
Dualmamba: A lightweight spectral-spatial mamba-convolution network for hyperspectral image classification
The effectiveness and efficiency of modeling complex spectral–spatial relations are crucial
for hyperspectral image (HSI) classification. Most existing methods based on convolution …
for hyperspectral image (HSI) classification. Most existing methods based on convolution …
V2x-vlm: End-to-end v2x cooperative autonomous driving through large vision-language models
Advancements in autonomous driving have increasingly focused on end-to-end (E2E)
systems that manage the full spectrum of driving tasks, from environmental perception to …
systems that manage the full spectrum of driving tasks, from environmental perception to …
DEMAE: Diffusion Enhanced Masked Autoencoder for Hyperspectral Image Classification With few Labeled Samples
Unlike other deep learning (DL) models, Transformer has the ability to extract long-range
dependency features from hyperspectral image (HSI) data. Masked autoencoder (MAE) …
dependency features from hyperspectral image (HSI) data. Masked autoencoder (MAE) …
SWDiff: Stage-Wise Hyperspectral Diffusion Model for Hyperspectral Image Classification
L Chen, J He, H Shi, J Yang, W Li - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Hyperspectral image classification (HSIC) has been a popular task in recent years. Even
benefiting from the rapid development of deep neural networks (DNNs), there are still …
benefiting from the rapid development of deep neural networks (DNNs), there are still …
Spatial-Spectral Enhancement and Fusion Network for Hyperspectral Image Classification with Few Labeled Samples
Deep learning has shown great potential in hyperspectral image (HSI) classification.
However, training these models usually requires a large amount of labeled data. Since the …
However, training these models usually requires a large amount of labeled data. Since the …
DWSDiff: Dual-Window Spectral Diffusion for Hyperspectral Anomaly Detection
W Chen, X Zhi, S Jiang, Y Huang… - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
Anomaly detection has emerged as a critical area of research in hyperspectral imagery
processing, focusing on detect sparse, small targets with spectral and spatial features …
processing, focusing on detect sparse, small targets with spectral and spatial features …
Diffusing Background Dictionary for Hyperspectral Anomaly Detection
Y Wu, Y Meng, L Sun - Proceedings of the Asian …, 2024 - openaccess.thecvf.com
The diffusion model (DM) has achieved remarkable results in image generation and has
been used in hyperspectral image (HSI) processing. However, DM has not been directly …
been used in hyperspectral image (HSI) processing. However, DM has not been directly …
EASKAM: enhanced adaptive source-selection kernel with attention mechanism for hyperspectral image classification
Abstract Hyperspectral Images (HSIs) possess extensive applications in remote sensing,
especially material discrimination and earth observation monitoring. However, constraints in …
especially material discrimination and earth observation monitoring. However, constraints in …
Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model
Y Zhu, L Xu - arxiv preprint arxiv:2502.19700, 2025 - arxiv.org
Although data augmentation is an effective method to address the imbalanced-small sample
data (ISSD) problem in hyperspectral image classification (HSIC), most methodologies …
data (ISSD) problem in hyperspectral image classification (HSIC), most methodologies …