Shallow Water Bathymetry Inversion Method Based on Spatiotemporal Coupling Correlation Adaptive Spectroscopy.
Saved in:
| Title: | Shallow Water Bathymetry Inversion Method Based on Spatiotemporal Coupling Correlation Adaptive Spectroscopy. |
|---|---|
| Authors: | Du, Jiaxing1,2 (AUTHOR), Li, Houpu1,2 (AUTHOR) lihoupu1985@126.com, Jia, Shuaidong1,2 (AUTHOR), Li, Gaixiao2 (AUTHOR), Dong, Jian2 (AUTHOR), Liu, Bing1 (AUTHOR), Bian, Shaofeng1 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p741. 22p. |
| Subjects: | Bathymetry, Spectrum analysis, Boosting algorithms, Spatial analysis (Statistics), Machine learning, Remote sensing |
| Geographic Terms: | Tampa Bay (Fla.) |
| Abstract: | Highlights: What are the main findings? A shallow water bathymetry inversion method based on spatiotemporal coupled adaptive spectroscopy has been proposed, which enables dynamic filtering of pixel-level features and effectively mitigates the interference of spatiotemporal heterogeneity on water bathymetry inversion. XGBoost model performs optimally with the support of this inversion method, achieving an R2 of 0.93 and an RMSE of 0.16 m, which is 56% lower than that of the traditional spectral inversion method. What are the implications of the main findings? Breaking through the limitations of traditional fixed feature combinations, it provides a new paradigm for multi-dimensional feature optimization in remote sensing water bathymetry inversion. Developing a low-cost, high-frequency shallow water bathymetry inversion scheme based on open-source data can provide high-precision bathymetry data support for scenarios such as nearshore marine monitoring and marine resource management. Shallow water bathymetry data underpins maritime shipping and marine resource survey/protection, but its accuracy is constrained by water heterogeneity and spectral interference. To address this, this study proposes a Spatio-Temporal Coupling and Correlation Adaptive Spectral (STCCAS) inversion method, integrating four machine learning models: Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Experiments were conducted in Tampa Bay's nearshore waters, using Sentinel-2 imagery and Airborne LiDAR Bathymetry (ALB) data. Core to STCCAS, the Temporal Stability Index (TSI) quantifies spectral temporal consistency, while the Normalized Difference Turbidity Index (NDTI) characterizes water turbidity, and the two indices synergistically form a dual-scale "spectral reliability-turbidity stability" evaluation system for pixel-level feature quality assessment—coupled with spectral fusion features and spatial location, they jointly realize pixel-level feature reliability weighting and dynamic filtering to build a water condition-adaptive input set. Comparative analysis of inversion performance under the original spectral features (OSFs) inversion method vs. STCCAS inversion method confirms STCCAS significantly boosts accuracy. XGBoost outperforms others, achieving a coefficient of determination (R2) of 0.93, root mean square error (RMSE) of 0.16 m, and mean absolute error (MAE) of 0.12 m. STCCAS breaks the limitations of traditional fixed feature combinations, effectively adapting to nearshore water heterogeneity. It provides a novel method for high-frequency, high-precision shallow water bathymetry inversion, with important practical value for marine environmental monitoring and resource management. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? A shallow water bathymetry inversion method based on spatiotemporal coupled adaptive spectroscopy has been proposed, which enables dynamic filtering of pixel-level features and effectively mitigates the interference of spatiotemporal heterogeneity on water bathymetry inversion. XGBoost model performs optimally with the support of this inversion method, achieving an R2 of 0.93 and an RMSE of 0.16 m, which is 56% lower than that of the traditional spectral inversion method. What are the implications of the main findings? Breaking through the limitations of traditional fixed feature combinations, it provides a new paradigm for multi-dimensional feature optimization in remote sensing water bathymetry inversion. Developing a low-cost, high-frequency shallow water bathymetry inversion scheme based on open-source data can provide high-precision bathymetry data support for scenarios such as nearshore marine monitoring and marine resource management. Shallow water bathymetry data underpins maritime shipping and marine resource survey/protection, but its accuracy is constrained by water heterogeneity and spectral interference. To address this, this study proposes a Spatio-Temporal Coupling and Correlation Adaptive Spectral (STCCAS) inversion method, integrating four machine learning models: Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Experiments were conducted in Tampa Bay's nearshore waters, using Sentinel-2 imagery and Airborne LiDAR Bathymetry (ALB) data. Core to STCCAS, the Temporal Stability Index (TSI) quantifies spectral temporal consistency, while the Normalized Difference Turbidity Index (NDTI) characterizes water turbidity, and the two indices synergistically form a dual-scale "spectral reliability-turbidity stability" evaluation system for pixel-level feature quality assessment—coupled with spectral fusion features and spatial location, they jointly realize pixel-level feature reliability weighting and dynamic filtering to build a water condition-adaptive input set. Comparative analysis of inversion performance under the original spectral features (OSFs) inversion method vs. STCCAS inversion method confirms STCCAS significantly boosts accuracy. XGBoost outperforms others, achieving a coefficient of determination (R2) of 0.93, root mean square error (RMSE) of 0.16 m, and mean absolute error (MAE) of 0.12 m. STCCAS breaks the limitations of traditional fixed feature combinations, effectively adapting to nearshore water heterogeneity. It provides a novel method for high-frequency, high-precision shallow water bathymetry inversion, with important practical value for marine environmental monitoring and resource management. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18050741 |
