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), which is based on Transformer architecture, employs a "mask-reconstruction" strategy for training, allowing the model...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 62; S. 1 - 16
Hauptverfasser:	Li, Ziyu, Xue, Zhaohui, Jia, Mingming, Nie, Xiangyu, Wu, Hao, Zhang, Mengxue, Su, Hongjun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Classification Coders Data models Deep learning Diffusion Diffusion barriers Diffusion models Feature extraction few labeled samples hyperspectral image (HSI) classification Hyperspectral imaging Image classification Image enhancement Image reconstruction Machine learning masked autoencoder (MAE) Signal classification Signal to noise ratio Simultaneous discrimination learning Source code Spatial discrimination learning State-of-the-art reviews Task analysis Training Transformer Transformers
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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