TMAR: 3-D Transformer Network via Masked Autoencoder Regularization for Hyperspectral Sharpening

Fusion-based hyperspectral super-resolution techniques are utilized to increase the spatial resolution of a hyperspectral image (HSI) by fusing it with a high spatial resolution assistive image. Transformers have shown high efficiency in vision tasks due to their ability to learn global and long-ran...

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Vydáno v:IEEE journal of selected topics in applied earth observations and remote sensing Ročník 18; s. 15845 - 15862
Hlavní autoři: Dehghan, Zeinab, Yang, Jingxiang, Yazdi, Mehran, Khader, Abdolraheem, Xiao, Liang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Asymmetric structures
Computational modeling
Convolution
Convolutional neural networks
Data mining
Feature extraction
Hyperspectral
Hyperspectral imaging
image fusion
Information processing
Mathematical models
multispectral
neural network
Neural networks
pansharpening
Regularization
Remote sensing
residual network
Spatial discrimination
Spatial resolution
super-resolution (SR)
Three-dimensional displays
Transformers
ISSN:1939-1404, 2151-1535
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Popis
Shrnutí:Fusion-based hyperspectral super-resolution techniques are utilized to increase the spatial resolution of a hyperspectral image (HSI) by fusing it with a high spatial resolution assistive image. Transformers have shown high efficiency in vision tasks due to their ability to learn global and long-range information. Many networks have utilized them in vision tasks such as super-resolution. However, the employment of convolutional neural networks (CNN) or transformers often results in considerable computational complexity. In addition, prior networks often overlook the regularization term separately, which is an absent factor in these networks. In this study, we focus on leveraging the power of CNN and transformer models and propose a multistage deep transformer-based super-resolution network that is regularized via an asymmetric autoencoder structure. In addition, we utilize a 3-D convolution layer in the light transformer structure because it allows for more flexible computation of correlations between HSI layers and better capturing of dependencies within spectral-spatial features. We apply a spectral masking autoencoder in an asymmetric structure to extract superior prior features from training data and regularize the network. Experimental results on remote sensing HSI datasets demonstrate that our proposed network provides superior efficiency compared to the state-of-the-art fusion-based super-resolution approaches.
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2025.3580093