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.
Peng Yu, Pu Song, Xiaojing Zhong et al.· IEEE Journal of Selected Top...· 0 citations
High-resolution topographic mapping of intertidal wetlands is essential for geomorphic analysis, yet existing remote sensing methods often struggle with vegetation interference, dependence on dense time-series data, and limited representation of fine geomorphic features. We propose a canopy-height-constrained stratified cooperative inversion framework for the entire intertidal wetland, integrating single-phase submeter optical imagery (Jilin-1), spaceborne photon-counting light detection and ranging (LiDAR) Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2), and machine learning. To accurately construct digital elevation model (DEM) and canopy height model (CHM) training samples in salt-marsh environments, we developed an ATL03 photon-classification workflow combining histogram-based control-point extraction and morphological refinement to generate these samples directly from ICESat-2 ATL03 photons. The retrieved CHM was then introduced as a structural constraint in the DEM retrieval model to support canopy-terrain signal decoupling in vegetated salt-marsh areas. A case study on Chongming Island, Shanghai, China, demonstrated that the DEM retrieval achieved high accuracy on the test set (R ${}^{2} =0.94$ , root-mean-squared error (RMSE) = 0.28 m) and maintained consistent performance against independent UAV-LiDAR validation data (R ${}^{2} = 0.53-0.77$ and RMSE = 0.34–0.53 m). The retrieved 0.5-m DEM reproduced regional elevation gradients, tidal-creek networks, and microtopographic variations across bare flats and vegetated marshes. Shapley additive explanation (SHAP) analysis showed that elevation retrieval over bare mudflats relied mainly on spectral predictors, whereas vegetated areas exhibited a complementary spectral-texture-CHM structure, with CHM consistently ranking as a mid-to-high predictor (fourth–seventh). This further supports the role of CHM as an effective structural constraint. By using only single-phase imagery and ATL03-derived DEM/CHM samples, the framework enables intertidal topographic retrieval that includes vegetated areas. It therefore provides an efficient and low-cost pathway for high-accuracy intertidal topographic monitoring under complex environmental conditions and limited image availability.
Zhenjie Yang, Weiwei Sun, Jianrong Zhu et al.· IEEE Transactions on Geoscie...· 0 citations