A Semi-Supervised Topographic Inversion Algorithm for Small-Scale Tidal Flats Based on Multi-Source Data Fusion Under Spatially Clustered ICESat-2 Label Distributions.
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| Title: | A Semi-Supervised Topographic Inversion Algorithm for Small-Scale Tidal Flats Based on Multi-Source Data Fusion Under Spatially Clustered ICESat-2 Label Distributions. |
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| Authors: | Chen, Hao1,2 (AUTHOR), Luo, Xiaowen1,2 (AUTHOR) luoxiaowen@sio.org.cn, Gui, Feng1,3 (AUTHOR), Cui, Jiaxin2,3,4 (AUTHOR), Chen, Jiayang1,2,4 (AUTHOR), Li, Qi1,2 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p2017. 30p. |
| Subjects: | Tidal flats, Multisensor data fusion, Deep learning, Digital elevation models, Loss functions (Statistics), Supervised learning |
| Abstract: | Highlights: What are the main findings? RAPS-UNet, a residual-attention U-Net coupled with a physics-constrained loss and confidence-weighted pseudo-label augmentation, achieves an RMSE of 0.20 m, an MAE of 0.16 m, and an R2 of 0.91 for tidal-flat DEM inversion against a field-surveyed reference, reducing field-validation RMSE by approximately 43% relative to the conventional inundation frequency–elevation linear model. A four-level validation protocol—covering in-distribution validation, spatial holdout testing, and field-based assessment over interpolation (RMSE = 0.17 m) and extrapolation (RMSE = 0.22 m) zones—demonstrates that the proposed model retains accuracy beyond the spatial footprint of clustered ICESat-2 tracks, where residual learning, attention-based multi-source fusion, physics-constrained loss, and pseudo-label augmentation together address feature heterogeneity, physical consistency, and label sparsity. What are the implications of the main findings? The proposed framework provides a practical approach to high-precision DEM retrieval over small-scale tidal flats (~1 km2 in spatial extent) where ICESat-2 elevation labels are sparse and spatially clustered, directly supporting fine-scale coastal monitoring, geomorphic change analysis, and intertidal ecological assessment. By coupling physics-constrained learning with confidence-weighted pseudo-label augmentation, the study extends supervised deep learning beyond its conventional dependence on dense, well-distributed labels, offering a potentially adaptable framework for remote-sensing inversion in other observation-sparse coastal and geomorphological settings. High-precision topography of tidal flats is essential for coastal monitoring, geomorphic change analysis, and ecological assessment. Although satellite remote sensing supports repeated and large-area observation, topographic inversion over small-scale tidal flats—here defined as localized intertidal patches with limited areal extent, represented in this study by a 1.11 km2 tidal flat near Dafeng Port—remains challenging, because ICESat-2 laser altimetry tracks across such areas are typically sparse and spatially clustered within narrow sub-regions, leaving extensive observation-blind zones without direct elevation labels. This label-clustering problem constrains the applicability of traditional empirical models and tends to cause deep learning models to generalize poorly beyond the spatial distribution of training samples. To address this issue, this study proposes a Residual Attention Physical-constraint Semi-supervised U-Net (RAPS-UNet) that fuses ICESat-2 ATL03/ATL08 elevation labels with Sentinel-1 SAR and Sentinel-2 optical features. The preprocessing pipeline comprises refined ICESat-2 photon filtering, adaptive inundation-frequency extraction, multi-source feature selection, and baseline DEM construction. RAPS-UNet integrates residual learning, attention-based multi-source fusion, physics-constrained loss, and confidence-weighted pseudo-label augmentation to improve extrapolation under clustered-label conditions. A four-level validation protocol—in-distribution validation, spatial holdout testing, and field-based assessment over both interpolation and extrapolation zones—was designed to evaluate spatial generalization. Against a field-surveyed DEM, RAPS-UNet achieved an overall RMSE of 0.20 m, an MAE of 0.16 m, and an R2 of 0.91; the field-based interpolation and extrapolation zones yielded RMSEs of 0.17 m and 0.22 m, respectively, while the spatial holdout test reached an RMSE of 0.23 m and an R2 of 0.81. Relative to the traditional inundation frequency–elevation linear model (RMSE = 0.35 m), RAPS-UNet reduced the field-validation RMSE by approximately 43%. The proposed framework therefore offers a practical approach for fine-scale coastal-zone topographic mapping under sparse and spatially clustered altimetry conditions. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? RAPS-UNet, a residual-attention U-Net coupled with a physics-constrained loss and confidence-weighted pseudo-label augmentation, achieves an RMSE of 0.20 m, an MAE of 0.16 m, and an R2 of 0.91 for tidal-flat DEM inversion against a field-surveyed reference, reducing field-validation RMSE by approximately 43% relative to the conventional inundation frequency–elevation linear model. A four-level validation protocol—covering in-distribution validation, spatial holdout testing, and field-based assessment over interpolation (RMSE = 0.17 m) and extrapolation (RMSE = 0.22 m) zones—demonstrates that the proposed model retains accuracy beyond the spatial footprint of clustered ICESat-2 tracks, where residual learning, attention-based multi-source fusion, physics-constrained loss, and pseudo-label augmentation together address feature heterogeneity, physical consistency, and label sparsity. What are the implications of the main findings? The proposed framework provides a practical approach to high-precision DEM retrieval over small-scale tidal flats (~1 km2 in spatial extent) where ICESat-2 elevation labels are sparse and spatially clustered, directly supporting fine-scale coastal monitoring, geomorphic change analysis, and intertidal ecological assessment. By coupling physics-constrained learning with confidence-weighted pseudo-label augmentation, the study extends supervised deep learning beyond its conventional dependence on dense, well-distributed labels, offering a potentially adaptable framework for remote-sensing inversion in other observation-sparse coastal and geomorphological settings. High-precision topography of tidal flats is essential for coastal monitoring, geomorphic change analysis, and ecological assessment. Although satellite remote sensing supports repeated and large-area observation, topographic inversion over small-scale tidal flats—here defined as localized intertidal patches with limited areal extent, represented in this study by a 1.11 km2 tidal flat near Dafeng Port—remains challenging, because ICESat-2 laser altimetry tracks across such areas are typically sparse and spatially clustered within narrow sub-regions, leaving extensive observation-blind zones without direct elevation labels. This label-clustering problem constrains the applicability of traditional empirical models and tends to cause deep learning models to generalize poorly beyond the spatial distribution of training samples. To address this issue, this study proposes a Residual Attention Physical-constraint Semi-supervised U-Net (RAPS-UNet) that fuses ICESat-2 ATL03/ATL08 elevation labels with Sentinel-1 SAR and Sentinel-2 optical features. The preprocessing pipeline comprises refined ICESat-2 photon filtering, adaptive inundation-frequency extraction, multi-source feature selection, and baseline DEM construction. RAPS-UNet integrates residual learning, attention-based multi-source fusion, physics-constrained loss, and confidence-weighted pseudo-label augmentation to improve extrapolation under clustered-label conditions. A four-level validation protocol—in-distribution validation, spatial holdout testing, and field-based assessment over both interpolation and extrapolation zones—was designed to evaluate spatial generalization. Against a field-surveyed DEM, RAPS-UNet achieved an overall RMSE of 0.20 m, an MAE of 0.16 m, and an R2 of 0.91; the field-based interpolation and extrapolation zones yielded RMSEs of 0.17 m and 0.22 m, respectively, while the spatial holdout test reached an RMSE of 0.23 m and an R2 of 0.81. Relative to the traditional inundation frequency–elevation linear model (RMSE = 0.35 m), RAPS-UNet reduced the field-validation RMSE by approximately 43%. The proposed framework therefore offers a practical approach for fine-scale coastal-zone topographic mapping under sparse and spatially clustered altimetry conditions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18122017 |
