Robust and Discriminative Visual-Semantic Alignment for Zero-Shot Remote Sensing Image Scene Classification

With the rapid development of remote sensing technology, the range of its applications, such as urban planning and environmental monitoring, has broadened considerably. However, the vast increase in remote sensing data poses challenges for training deep neural networks, primarily due to the extensive labeled datasets required. To address this issue, zero-shot learning (ZSL) emerges as a viable approach, enabling recognition of unseen categories without relying on training samples. In this paper, we introduce a Robust and Discriminative Visual-Semantic Alignment (RDVSA) model, which utilizes attribute-guided contrastive learning for enhanced performance. By incorporating category centroids into the embedding space of visual features and attribute descriptions, our model enhances the discriminative power of learned features and stabilizes the learning process, particularly in the presence of complex data distributions and noise in remote sensing images. Additionally, we employ a Vision Transformer (ViT) and a “mean-teacher" framework to capture both global context and local details, ensuring robustness in occluded or irrelevant attribute regions. Comprehensive experiments on large-scale remote sensing benchmarks reveal that our approach surpasses current state-of-the-art methods in both ZSL and generalized zero-shot learning (GZSL) scenarios. These results underscore the effectiveness of using attribute-based contrastive learning alongside consistency constraints.