Adversarially Robust Hyperspectral Image Classification via Random Spectral Sampling and Spectral Shape Encoding

被引:10
|
作者
Park, Sungjune [1 ]
Lee, Hong Joo [1 ]
Ro, Yong Man [1 ]
机构
[1] Korea Adv Inst Sci & Technol KAIST, Sch Elect Engn, Daejeon 34141, South Korea
关键词
Training; Robustness; Spectral shape; Shape; Encoding; Feature extraction; Hyperspectral imaging; Adversarial robustness; hyperspectral image classification; random spectral sampling; spectral shape encoding; NEURAL-NETWORKS; ATTACKS; THREAT;
D O I
10.1109/ACCESS.2021.3076225
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Although the hyperspectral image (HSI) classification has adopted deep neural networks (DNNs) and shown remarkable performances, there is a lack of studies of the adversarial vulnerability for the HSI classifications. In this paper, we propose a novel HSI classification framework robust to adversarial attacks. To this end, we focus on the unique spectral characteristic of HSIs (i.e., distinctive spectral patterns of materials). With the spectral characteristic, we present the random spectral sampling and spectral shape feature encoding for the robust HSI classification. For the random spectral sampling, spectral bands are randomly sampled from the entire spectrum for each pixel of the input HSI. Also, the overall spectral shape information, which is robust to adversarial attacks, is fed into the shape feature extractor to acquire the spectral shape feature. Then, the proposed framework can provide the adversarial robustness of HSI classifiers via randomization effects and spectral shape feature encoding. To the best of our knowledge, the proposed framework is the first work dealing with the adversarial robustness in the HSI classification. In experiments, we verify that our framework improves the adversarial robustness considerably under diverse adversarial attack scenarios, and outperforms the existing adversarial defense methods.
引用
收藏
页码:66791 / 66804
页数:14
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