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.

The joint classification of hyperspectral images (HSIs) and LiDAR data plays a crucial role in earth observation missions. Most advanced methods are based on discrete label supervision. However, since discrete labels only convey limited information that a sample belongs to a single definite class and lack of prior information, it is difficult to supervise the model to capture rich inherent semantic information in complex data distributions, hindering the classification performance. To this end, we propose a text-supervised multidimensional contrastive fusion network, termed as TMCFN, which leverages class text information to guide the learning of visual representations while establishing a semantic association of text and visual features for classification by using multidimensionally incorporated contrastive learning (CL) paradigms. Specifically, TMCFN is composed of text information encoding (TIE), visual features representation (VFR) and text-visual features alignment and classification (TVFAC). TIE is employed to extract semantic information from class text extended from class names, intrinsic attributes and inter-class relationships. VFR mainly comprises a new fusion-based contrastive feature learning module (FCFLM) to extract discriminative visual features and a text-guided attention feature fusion module (TAF2M) to fuse visual features under the guidance of text information. TVFAC optimizes the learning of visual features under the supervision of text information while using a CL paradigm to align text and visual features for establishing the semantic association, and achieves the classification by directly computing the similarity between the visual features and each text feature without an additional classifier. Experiments with three standard datasets verify the effectiveness of TMCFN.