Cross-Scene Classification of Hyperspectral Images via Generative Adversarial Network in Latent Space

Classifying high-dimensional hyperspectral image (HSI) with limited labeled samples is a difficult problem. One effective solution is to leverage knowledge from scenes with well-labeled image (the source domain) to aid training in the target domain. However, since the source and target domains have different category spaces, it is crucial to extract more discriminative features and address domain adaptation challenges. To tackle this issue, we propose a cross-scene classification method for HSIs via generative adversarial networks (GANs) in latent space (GLS). Our method employs autoencoders (AEs) to map the input data to a latent space, where the most effective feature representation is extracted and preserved by deep residual 3D convolutional neural networks (CNN). The unlabeled samples in the target domain are also utilized in the AE which ensure all the samples are considered. We leverage conditional adversarial domain adaptation to overcome the domain shift, and introduce maximum mean discrepancy loss to minimize distribution differences between the two domains, facilitating better domain distribution alignment. We tested our approach on three public datasets and demonstrated that it outperforms existing few-shot learning methods. Our results highlight the effectiveness of our classification method via GANs in latent space for HSIs, and show that it has potential for practical applications.