DF-PEM: Dual-Flow Prompt-Expert Mamba for Multimodal Remote Sensing Incremental Classification

Existing deep-learning-based Multimodal Remote Sensing Imagery (MRSI) classification models rely on fixed-category paradigms and struggle to adapt to novel categories, primarily due to the catastrophic forgetting of vintage knowledge, which substantially impedes their practical applications. To overcome this challenge, we propose a novel Dual-Flow Prompt-Expert Mamba (DF-PEM) network for MRSI incremental classification, which incorporates dedicated parallel branches for spectral and spatial feature extraction, synergized with a cross-modal fusion module to integrate modality-specific representations. The primary objective of DF-PEM is to capture discriminative MRSI features while preserving knowledge of previously seen categories during the incremental learning process. Specifically, DF-PEM integrates the Prompt-driven Assistant-Expert (PAE) mechanism within the Mamba architecture to dynamically activate category-specific prompt experts per input, enabling seamless adaptation to emerging categories while mitigating knowledge erosion of prior classes. Furthermore, a Spherical Gaussian Sift (SGS) strategy is developed in the initial stage, which models the complex feature distributions of historical categories and neutralizes noise and outlier effects, yielding robust representative prototypes. Finally, a Prototype-aware Regularization Classifier (PRC) that adaptively balances prediction bias between incremental and old categories by learning regularization coefficients, which strategically penalize parameter updates that deviate the model from historical knowledge, further enhancing knowledge retention and generalization. Extensive experiments on benchmark datasets demonstrate that the proposed DF-PEM significantly outperforms existing methods, validating its effectiveness for MRSI classification tasks.