Frequency and Prompt Learning Cooperation Enhanced Mamba for Remote Sensing Semantic Segmentation

Very high-resolution remote sensing images is a key data source for space–air–ground collaborative monitoring, supplying fine-grained spatial information crucial to urban management, disaster response and ecological assessment. Reliable extraction of roads, buildings and vegetation therefore hinges on accurate semantic segmentation. Recently, the Mamba state-space model has been widely adopted for remotesensing semantic segmentation, markedly improving the globalcontext modelling capacity of existing approaches; nevertheless, the severe inter-class pixel imbalance among categories persists, continuing to hamper segmentation performance. To mitigate this issue, we present FPMamba, a hybrid network that couples frequency-domain cues with prompt learning to emphasize rare classes and suppress background clutter. On the frequency side, a Frequency Channel–Spatial Attention Module (FCSAM) is inserted into each VSS block to fuse spatial features with a frequency branch that captures high-frequency details, markedly sharpening edge and texture representations. A Frequency Fusion Attention Module (FFAM) is further deployed on every skip connection to adaptively reconcile highlevel semantics with low-level details. On the prompt side, we design a lightweight Prompt Text-Guided Strategy (PTGS) that aligns category prompts with image patches via Contrastive Language–Image Pre-training (CLIP), producing CLIP-guided attention mapsthat guide the network towards long-tail targets. PTGS and FCSAM are jointly embedded into a dual-enhanced VSS block, completing the FPMamba architecture. Comprehensive tests on the ISPRS benchmarks demonstrate that FPMamba reaches an overall accuracy of 88.97% and a MIoU of 75.61% on the Vaihingen dataset, while achieving 87.53% overall accuracy and 75.41% MIoU on the Potsdam dataset, outperforming current state-of-the-art methods. These results corroborate the effectiveness and transferability of integrating frequency-domain information with prompt learning for remote-sensing semantic segmentation. Code will be publicly released upon publication.