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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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 16
Main Authors: Li, Ziyu, Xue, Zhaohui, Jia, Mingming, Nie, Xiangyu, Wu, Hao, Zhang, Mengxue, Su, Hongjun
Format: Journal Article
Language:English
Published: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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 to be effective for downstream tasks. However, existing MAE-based methods only apply spectral or spatial masking to HSI and reconstruct them for feature learning, which is too simplistic and insufficient for the model to learn robust features. Additionally, the issue of lacking labeled samples in HSI and the primary objective of MAE to reduce the reliance on labeled samples are often overlooked. To address these issues, we are inspired by diffusion-based representation learning and propose diffusion-enhanced MAE (DEMAE) for HSI classification with few labeled samples. First, an asymmetric encoder-decoder framework is constructed as the backbone by stacking both conditional and standard Transformer blocks. Second, we devise an auxiliary task aimed at simultaneous denoising and reconstruction, facilitating heuristic feature learning from HSI data. Third, the encoder of DEMAE is isolated for training with few labeled samples. Finally, the encoder is used for classification, and a novel signal-to-noise ratio enhanced (SNR-Enhanced) loss function is introduced to regularize the model training process. The performance of DEMAE is evaluated on four benchmark datasets, demonstrating its superiority in classification accuracy and mapping capabilities on unlabeled areas compared to existing state-of-the-art methods with few labeled samples. The source code will be available online at https://github.com/ZhaohuiXue/DEMAE .
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3445991