Multiscale Feature-Enhanced Water Body Detector of Truncated Gaussian Clutter in SAR Imagery
This article presents a multiscale feature-enhanced water detector using truncated Gaussian clutter (TGCFeWD) for synthetic aperture radar (SAR) imagery. It aims to improve detection accuracy through adaptive elimination of high-intensity outliers and shadows. High-intensity outliers cause overestim...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10816489/ |
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| Summary: | This article presents a multiscale feature-enhanced water detector using truncated Gaussian clutter (TGCFeWD) for synthetic aperture radar (SAR) imagery. It aims to improve detection accuracy through adaptive elimination of high-intensity outliers and shadows. High-intensity outliers cause overestimation of parameters used for water data statistical modeling, resulting in inaccurate thresholds. Furthermore, shadows in SAR imagery have similar digital numbers and statistical parameters to water, making them difficult to distinguish. To address these two key issues, this article proposes a comprehensive approach comprised of three main components: Part A is to calculate Gaussian distance based on which the land surfaces or artificial objects can be removed easily by Otsu. Part B is to expand the difference between water and shadows through feature enhancing. Part C is to segment water and shadows according to the expanding differences. The proposed TGCFeWD method effectively detects water bodies including seas, rivers, and lakes. Compared to several existing methods, TGCFeWD greatly improves water detection accuracy in complex environments. Based on metrics of accuracy, <italic>F</italic>1, and mean of intersection over union, TGCFeWD achieves the best performance (92.4%, 82.4%, and 80.1% for all data with five water body types) compared to several traditional methods, and even outperforms some neural-network-based methods in certain scenarios. The results are validated on the HISEA flooding dataset. |
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| ISSN: | 1939-1404 2151-1535 |