Knowledge Graph Enhanced for Zero-Shot Semantic Segmentation in Remote Sensing Imagery
Abstract. Zero-shot semantic segmentation (ZSSS) is a crucial task in remote sensing image understanding, yet existing methods still suffer from limited generalization to unseen classes. To address this issue, we propose a Knowledge Graph (KG) enhanced ZSSS framework, which introduces explicit hierarchical and relational information into class embeddings to achieve more structured and semantically consistent representations. Specifically, a KG class encoder is designed, consisting of the class enhanced query (CEQ) and class enhanced embedding (CEE) modules, which extract class-relevant subgraphs from a self-constructing Remote Sensing Semantic Class Knowledge Graph (RSSCKG) and generate knowledge-enriched embeddings through a text encoder. Experiments on three public remote sensing datasets demonstrate that the proposed method consistently improves performance across seven state-of-the-art ZSSS frameworks. The integration of KG-based embeddings yields significant gains in the evaluation metrics, with particularly strong improvements on unseen classes, while maintaining accuracy on seen classes. Compared with enhancement strategies based on large language model (LLM) generated descriptions, the proposed KG class encoder exhibit superior semantic separability and stability. These results validate the effectiveness, generalization, and scalability of the proposed framework for ZSSS in remote sensing imagery.