CoastMamba: A Boundary-Enhanced Mamba Framework for Sea–Land Segmentation in Optical Remote Sensing Imagery
Sea–land segmentation (SLS) in optical remote sensing imagery is a fundamental task that faces significant challenges due to the complex morphology of coastlines. Shaped by diverse natural factors and anthropogenic infrastructure, coastal environments exhibit substantial spatial–temporal variability and boundary ambiguity. Existing convolutional neural networks (CNN)-based and vision Transformers-based methods often suffer from high computational costs and insufficient exploitation of frequency-domain cues, which are critical for boundary characterization. To address these issues, we propose CoastMamba, a novel SLS framework. Specifically, to mitigate background interference and structural ambiguity, a grouped coordinate Mamba (GCMamba) block is designed to generate adaptive gating masks for effective feature recalibration and selective boundary emphasis. Moreover, to handle weak contrast and blurred boundaries, a Frequency-Domain Boundary-Enhanced Module is introduced to jointly leverage spatial and frequency representations, enhancing feature discrimination. Furthermore, to preserve fine-grained local details alongside global semantics, a multilevel feature aggregation pyramid (MFAP) decoder is employed to integrate hierarchical features. Finally, to address the limitations of existing SLS datasets regarding low spatial resolution and limited scene coverage, we construct the high-resolution fine-grained Minnan Sea–Land Segmentation dataset. Extensive experiments on this dataset and public benchmarks demonstrate that CoastMamba achieves a boundary intersection over union (IoU) of 60.06%, an IoU of 96.84%, and an F1-score of 98.39%, significantly outperforming state-of-the-art methods.