Hyperspectral Image Classification With Adversarial Attack

The performance of a neural network is highly dependent on the labeled samples. However, the labeled samples are primarily clean, which prevents the network from capturing the features of the samples near the decision boundary. For hyperspectral images (HSIs), high spectral dimensions and same-spectra foreign matter lead to more boundary samples in the data. In this letter, we investigate an adversarial attack algorithm against these problems for HSIs. A modified DeepFool algorithm is implemented to generate boundary adversarial samples with minimal disturbance, and the generated boundary adversarial samples are simply added to the training set to improve the accuracy of the boundary samples in the data. Furthermore, we iteratively complete network training and boundary adversarial sample generation so that the decision boundary can be adjusted according to the real-time classification situation. Extensive experiments are carried out on the two HSI datasets, and the results demonstrate that the modified DeepFool algorithm can improve the accuracy of the decision boundary. Our findings also show that adversarial attacks are sensitive to high-dimensional and multiple-category data and are worthy of further study.