Category-Level Positive–Negative Prompting for Open-Set Cross-Scene Remote Sensing Image Classification

Abstract

Cross-scene remote sensing (RS) image classification is crucial for urban analysis and environmental monitoring, but often suffers from distribution shifts caused by different imaging conditions. While unsupervised domain adaptation (UDA) can alleviate cross-scene discrepancies, its closed-set assumption is violated when unknown categories emerge in the target domain, thereby motivating the open-set domain adaptation (OSDA) setting. Existing OSDA methods remain limited in semantic modeling, since conventional visual approaches lack high-level semantic guidance and recent vision–language model (VLM)-based methods predominantly rely on positive prompts that encode only class-belonging semantics, providing limited cues for unknown-sample rejection. To address these issues, we propose category-level positive–negative prompting (CLPNP) for open-set cross-scene RS image classification. CLPNP integrates learnable positive prompts and multiple groups of negative prompts into a frozen VLM to jointly model belonging and nonbelonging semantics, thereby forming a category-level binary semantic discrimination mechanism. Built upon this prompt design, a positive–negative semantic-guided known–unknown separation (PNKUS) strategy is proposed, which establishes reliable binary semantic boundaries from labeled source data and transfers this discriminative structure to the target domain through uncertainty regularization, together with structured regularization on positive and negative prompts to ensure semantic diversity and separability. In addition, a threshold-free open-set inference mechanism is introduced by directly comparing positive and negative semantic responses for each known category. Extensive experiments on 18 cross-scene transfer tasks under two benchmark settings demonstrate that CLPNP consistently outperforms state-of-the-art OSDA methods and achieves a favorable balance between known-category recognition and unknown-category rejection.