Features kept generative adversarial network data augmentation strategy for hyperspectral image classification

In recent years, significant breakthroughs have been achieved in hyperspectral image (HSI) processing using deep learning techniques, including classification, object detection, and anomaly detection. However, the practical application of deep learning in HSI processing is limited by challenges such as small-sample size and sample imbalance issues. To mitigate these limitations, we propose a novel data augmentation strategy called Feature-Preserving Generative Adversarial Network Data Augmentation (FPGANDA). What sets our data augmentation strategy apart from existing generative model-based approaches is that we preserve the main spectral bands of HSI data using a newly designed band selection method. Additionally, our proposed generative model generates synthetic spectral bands, which are combined with the real spectral bands using a mixture strategy to create augmented data. This approach ensures that the augmented data retain the main features of the original data while also incorporating diverse features from the generated data. We evaluate our method on three different HSI datasets, comparing it with state-of-the-art techniques. Experimental results demonstrate that our proposed method significantly improves classification performance in most scenes and exhibits remarkable compatibility.