Adversarial Remote Sensing Scene Classification Based on Lie Group Feature Learning

Convolutional Neural Networks have been widely used in remote sensing scene classification. Since this kind of model needs a large number of training samples containing data category information, a Generative Adversarial Network (GAN) is usually used to address the problem of lack of samples. However, GAN mainly generates scene data samples that do not contain category information. To address this problem, a novel supervised adversarial Lie Group feature learning network is proposed. In the case of limited data samples, the model can effectively generate data samples with category information. There are two main differences between our method and the traditional GAN. First, our model takes category information and data samples as the input of the model and optimizes the constraint of category information in the loss function, so that data samples containing category information can be generated. Secondly, the object scale sample generation strategy is introduced, which can generate data samples of different scales and ensure that the generated data samples contain richer feature information. After large-scale experiments on two publicly available and challenging datasets, it is found that our method can achieve better scene classification accuracy even with limited data samples.

Automated summary

A novel supervised adversarial Lie Group feature learning network is proposed to effectively generate data samples with category information even with limited data samples. This model takes category information and data samples as input and optimizes the constraint of category information in the loss function, producing data samples containing category information. Additionally, an object scale sample generation strategy is introduced to generate data samples of different scales, ensuring the generated samples contain richer feature information.