Abstract
Hyperspectral image classification grapples with the twin challenges of high dimensionality and limited labelled data. These limitations hinder the development of generalizable classification models that can perform well across diverse datasets. To overcome these limitations, this paper proposes a novel semi-supervised framework that synergizes autoencoders, generative adversarial networks and zero-shot learning. This semi-supervised approach significantly improves feature extraction and data augmentation by harnessing the power of generative adversarial networks built upon autoencoders, ultimately enhancing classification accuracy. It further pushes the boundaries beyond traditional methods by enabling zero-shot learning, allowing the model to classify unseen data from classes not present in the training set. Additionally, the proposed model incorporates text embeddings to enrich feature representation, resulting in improved performance. This multimodal classification approach empowers the way for robust training and testing on cross-sensor datasets, even handling data with diverse spectra. Experimentally, it demonstrates remarkable accuracy across various domains, achieving a peak performance of 92.35% for cross-domain data and 91.83% for same-domain data, marking a significant leap forward in the generalizability of semi-supervised classification models.
