Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders

• URL: http://arxiv.org/abs/2207.07803v1
• Date: Sat, 16 Jul 2022 01:33:13 GMT
• Title: Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders
• Authors: Jiahao Qi, Zhiqiang Gong, Xingyue Liu, Kangcheng Bin, Chen Chen, Yongqian Li, Wei Xue, Yu Zhang, and Ping Zhong
• Abstract summary: We propose a masked spatial-spectral autoencoder (MSSA) for enhancing the robustness of HSI analysis systems. To improve the defense transferability and address the problem of limited labelled samples, MSSA employs spectra reconstruction as a pretext task. Comprehensive experiments over three benchmarks verify the effectiveness of MSSA in comparison with the state-of-the-art hyperspectral classification methods.
• Score: 15.839321488352535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community. However, it also makes HSI analysis systems vulnerable to adversarial attacks. To this end, we propose a masked spatial-spectral autoencoder (MSSA) in this paper under self-supervised learning theory, for enhancing the robustness of HSI analysis systems. First, a masked sequence attention learning module is conducted to promote the inherent robustness of HSI analysis systems along spectral channel. Then, we develop a graph convolutional network with learnable graph structure to establish global pixel-wise combinations.In this way, the attack effect would be dispersed by all the related pixels among each combination, and a better defense performance is achievable in spatial aspect.Finally, to improve the defense transferability and address the problem of limited labelled samples, MSSA employs spectra reconstruction as a pretext task and fits the datasets in a self-supervised manner.Comprehensive experiments over three benchmarks verify the effectiveness of MSSA in comparison with the state-of-the-art hyperspectral classification methods and representative adversarial defense strategies.

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