Recent research on the joint classification of multimodal remote sensing data has achieved outstanding performance in tasks within predefined label spaces. However, surface conditions are dynamic and change over time, resulting in variations in land cover classes collected from the same region at different time points. As a result, when new classes are discovered, the previous works must use a combination of old and new class data to retrain the model, which incurs high computational costs and raises concerns about data privacy. In this work, we propose the prototype-based meta-prompt tuning (PMPT) framework, which fine-tunes only a few session-relevant visual prompts to adapt to incremental classes, while simultaneously learning prototype embeddings for each class to preserve historical knowledge. Specifically, the PMPT consists of a meta-learning-based feature representation backbone and an incrementally updated nearest-class-mean (NCM) classifier. The backbone is trained on base class data to learn shared and stable global knowledge, then frozen, with only the prompts fine-tuned to extract sessions-specific local knowledge from incremental sessions. The NCM classifier is a globally shared classifier that measures the similarity between test samples and prototypes, effectively alleviating the issues of knowledge forgetting and overfitting. Additionally, we propose an incremental prototype contrastive loss to reduce semantic drift and prototype overlap in the embedding space. During the testing phase, the PMPT reproduces the complete embedding function by matching samples, class prototypes, and visual prompts, thereby enabling accurate classification of unknown samples. The method has been tested on widely used multimodal remote sensing datasets, demonstrating the effectiveness of the proposed PMPT in addressing the dilemma of stability-plasticity with limited incremental samples. The code is available at https://github.com/Jiahuiqu/PMPT.

Multimodal remote sensing image classification (RSIC) has emerged as a key focus in Earth observation, driven by its capacity to extract complementary information from diverse sources. Existing methods struggle with modality absence caused by weather or equipment failures, leading to performance degradation. As a solution, knowledge distillation-based methods train student networks (SN) using a full-modality teacher, but they usually require training separate SN for each modality absence scenario, increasing complexity. To this end, we propose a unified Distillation Prompt Mamba (DPMamba) framework for multimodal RSIC with missing modalities. DPMamba leverages knowledge distillation in a shared text semantic space to optimize learnable prompts, transforming them from ``placeholder" to ``adaptation" states by enriching missing modality information with full-modality knowledge. To achieve this, we focus on two main aspects: first, we propose a new modality-aware Mamba for dynamically and hierarchically extracting cross-modality interactive features, providing richer, contextually relevant representations for backpropagation-based optimization of prompts; and second, we introduce a novel text-bridging distillation method to efficiently transfer full-modality knowledge, guiding the inclusion of missing modality information into prompts. Extensive evaluations demonstrate the effectiveness and robustness of the proposed DPMamba.