The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case
Abstract
Parametric thresholding algorithms applied to synthetic aperture radar (SAR) imagery typically require the estimation of two distribution functions, i.e., one representing the target class and one its background. They are eventually used for selecting the threshold that allows binarizing the image in an optimal way. In this context, one of the main difficulties in parameterizing these functions originates from the fact that the target class often represents only a small fraction of the image. Under such circumstances, the histogram of the image values is often not obviously bimodal and it becomes difficult, if not impossible, to accurately parameterize distribution functions. Here we introduce a hierarchical split-based approach that searches for tiles of variable size allowing the parameterization of the distributions of two classes. The method is integrated into a flood-mapping algorithm in order to evaluate its capacity for parameterizing distribution functions attributed to floodwater and changes caused by floods. We analyzed a data set acquired during a flood event along the Severn River (U.K.) in 2007. It is composed of moderate (ENVISAT-WS) and high (TerraSAR-X)-resolution SAR images. The obtained classification accuracies as well as the similarity of performance levels to a benchmark obtained with an established method based on the manual selection of tiles indicate the validity of the new method.
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Cited by
DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J. W., & Lang, M. W. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111664 · Cited by 518
A review of remote sensing applications for water security: Quantity, quality, and extremes
Chawla, I., Karthikeyan, L., & Mishra, A. K. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124826 · Cited by 363
A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery
Liang, J., & Liu, D. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.10.017 · Cited by 328
Bonafilia, D., Tellman, B., Anderson, T., & Issenberg, E. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00113 · Cited by 295
Inundation Extent Mapping by Synthetic Aperture Radar: A Review
Shen, X., Wang, D., Mao, K., Anagnostou, E., & Hong, Y. (2019) · Remote Sensing · doi:10.3390/rs11070879 · Cited by 268
Metadata
- Authors
- Chini, M., Hostache, R., Giustarini, L., & Matgen, P.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2017-12
- Volume
- 55
- Issue
- 12
- Pages
- 6975-6988
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2751895300
- Cited by
- 334
- References
- 35
- FWCI
- 16.81
- Type
- journal article
- Language
- en
- Open access
- closed
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