BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images

Class-incremental learning aims to continuously expand the category space while mitigating catastrophic forgetting of previously learned classes, and has recently attracted increasing attention in remote sensing image classification for long-term adaptive applications. However, most existing class-incremental learning methods are developed under single-source classification settings, where the complementary information across multiple data sources is not fully exploited, limiting their applicability in multi-source remote sensing scenarios classification. To address this issue, we propose a novel prompt-based framework for class-incremental learning on multi-source remote sensing images. Specifically, we introduce a Bidirectional Cross-Modal Prompt Tuning (BiCM-PT) module that dynamically selects modality-specific prompts while preserving historical cross-modal relationships by freezing modality relation projectors from previous tasks, thereby enhancing model stability. Furthermore, to improve plasticity for new-class learning, we design a Prompt-Guided Knowledge Aggregator (PGKA) that leverages learned prompts to guide decision-level feature aggregation and extract discriminative multi-modal representations. Together, these components enable effective and stable class-incremental learning in multi-source remote sensing environments. Extensive experiments on three real-world remote sensing benchmarks demonstrate the effectiveness of our approach in balancing stability and plasticity under multi-source incremental learning settings. The code is available at https://github.com/Jiahuiqu/BiCMPT.