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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Vydané v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 18; s. 15845 - 15862
Hlavní autori:	Dehghan, Zeinab, Yang, Jingxiang, Yazdi, Mehran, Khader, Abdolraheem, Xiao, Liang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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