A Guided Implicit Generative Adversarial CNN–Transformer Framework for Class-Imbalanced Hyperspectral Image Classification

Abstract

Hyperspectral image (HSI) classification is important for land-cover recognition and remote-sensing scene analysis. However, class imbalance severely limits performance when minority classes contain only a few labeled samples, causing models to show strong overall results while failing to identify minority classes effectively. We propose a 3D guided implicit generative adversarial oversampling model (3D-GIGAMO), a generation–classification collaborative framework that alleviates class imbalance by producing class-conditioned minority-class patches for task-oriented oversampling, while learning discriminative spectral–spatial representations for robust and class-balanced HSI classification. A guided implicit neural representation conditioned on real training patches is embedded in an adversarial learning framework, and optimization is regularized using a conditional bounded-score objective with gradient-norm regularization. A hybrid convolutional neural network (CNN)–Transformer classifier is trained on both real and synthesized patches, and a class-consistency loss encourages the synthesized patches to preserve target-class semantics. Experiments on four public benchmarks (Indian Pines, Pavia University, WHU-Hi-LongKou, and Salinas) show improvements in both overall and average accuracy across all datasets, achieving average accuracies of 99.58%, 98.45%, 96.88%, and 99.85%, respectively, outperforming several recent methods.