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Cross-Dataset Model Training for Hyperspectral Image Classification Using Self-Supervised Learning
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AI
DOI：10.1109/TGRS.2024.3493969.png)
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摘要
En 中文
With the development of deep learning and the increase in the amount of data, general artificial intelligence models have become a popular research area nowadays. When facing a new application scenario, a pretraining general model can often show better performance than models trained with new data on its own. However, because of the specificity of the differences in hyperspectral image data bands, the current hyperspectral image classification (HSIC) field has not proposed a better general model training solution, and it is difficult to utilize the information of the existing hyperspectral datasets for model training in the face of a new scenario. In order to solve this problem, this article proposes a generalized hyperspectral classification model training method, which effectively completes the training of hyperspectral classification models across datasets by adaptive channel module and masked self-supervised pretraining method, and can pretrain and fine-tune hyperspectral classification models using multiple datasets. The adaptive channel module is able to solve the band difference problem of using hyperspectral datasets across datasets, and the masked self-supervised learning method solves the label difference and labeling difficulties of training models across datasets. Experimental results on multiple datasets show that the method proposed in this article can effectively use a large amount of data to complete the pretraining of hyperspectral classification models, and the fine-tuning results on downstream datasets have certain advantages relative to current advanced deep learning methods.
Keyword:
Hyperspectral imaging
Training
Data models
Feature extraction
Image classification
Adaptation models
Transformers
Self-supervised learning
Supervised learning
Deep learning
Classification
cross dataset
general model
hyperspectral image
masked autoencoder
self-supervised learning
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
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