• Corpus ID: 239050243

3D-ANAS v2: Grafting Transformer Module on Automatically Designed ConvNet for Hyperspectral Image Classification

@article{Xue20213DANASVG,
  title={3D-ANAS v2: Grafting Transformer Module on Automatically Designed ConvNet for Hyperspectral Image Classification},
  author={Xizhe Xue and Haokui Zhang and Zongwen Bai and Ying Li},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.11084}
}
Hyperspectral image (HSI) classification has been a hot topic for decides, as Hyperspectral image has rich spatial and spectral information, providing strong basis for distinguishing different land-cover objects. Benefiting from the development of deep learning technologies, deep learning based HSI classification methods have achieved promising performance. Recently, several neural architecture search (NAS) algorithms are proposed for HSI classification, which further improve the accuracy of… 
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References

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TLDR
A 3D convolutional neural network framework is proposed for accurate HSI classification, which is lighter, less likely to over-fit, and easier to train, and requires fewer parameters than other deep learning-based methods.
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TLDR
A 3-D asymmetric neural network search algorithm is proposed and used to automatically search for efficient architectures for HSI classifications, and a new fast classification framework is proposed, i.e., pixel-to-pixel classification framework, which has no repetitive operations and reduces the overall cost.
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TLDR
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TLDR
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TLDR
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TLDR
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