Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask
Abstract
:1. Introduction
- Assess the method performance, namely, the similarity between the SAR and reference masks;
- Select polarization and speckle filtering parameters to ensure maximum similarity between SAR and reference masks;
- Compare the results of the proposed method with the results obtained by the classical Otsu method;
- Learn how to construct SAR masks on days when it is not possible to generate a reference mask from optical data due to cloudiness.
2. Materials and Methods
2.1. Study Area
2.2. Satellite Data
- Apply orbit file—applying a satellite position and velocity parameters.
- Calibration— backscatter coefficient calculation.
- Speckle filtering—speckle noise filtering.
- Terrain correction—elimination of distortions due to oblique image geometry using a digital elevation model (Copernicus 30 m Global DEM).
- Linear-to-dB—converting to decibels (dB).
2.3. Water Body Detection Methods
2.3.1. Optical Sensors
2.3.2. SAR: Basic Method
- 1.
- Generating a backscattering coefficient histogram from SAR data in one of the polarizations.
- 2.
- Determining a threshold value ()—the minimum on the histogram that separates two modes.
- 3.
- Construction of a binary mask: .
- 1.
- Removing outliers. The values below the quantile or above the quantile were replaced by the corresponding quantile ( in the paper).
- 2.
- Constructing the distribution density function (kernel density estimation with Gaussian kernel).
- 3.
- Computing a local minimum of the distribution density.
- 4.
- Checking if the minimum is reached at one of the histogram edges. If not, the desired threshold value is found.
2.3.3. SAR: Proposed Method
2.4. Similarity Measures
- True positive (TP)— pixels are correctly identified as positive (“water”);
- False positive (FP)— pixels are wrongly identified as positive (“water”);
- False negative (FN)— pixels are wrongly identified as negative (“land”).
3. Results
3.1. IoU-Similarity and SAR Mask Quality
3.2. Selection of Polarization and Speckle Filtering Parameters
3.2.1. Maximum Mask Similarity
3.2.2. Sensitivity Analysis
- —Gamma Map, Lee, Lee Sigma, Median filters with a kernel size of 7 × 7;
- , —Gamma Map, Lee, Lee Sigma, Median filters with kernel sizes 5 × 5 and 7 × 7.
3.3. Comparison with the Otsu Method
3.3.1. Similarity When the Reference Mask Is Available
3.3.2. Threshold Arrangement
3.3.3. Similarity without Reference Masks
- 3 July 2018–15 July 2018;
- 16 June 2020–15 August 2020;
- 11 June 2021–29 June 2021;
- 11 June 2021–28 August 2021.
4. Discussion
4.1. Analysis of the Results Discrepancies
4.1.1. Discrepancies in Reference Masks
4.1.2. Variations in Accuracy of the Results
- The presence of false positive noise on water masks , which cannot be filtered (Figure 16c,f).
- Peculiarities of speckle filtering, due to the large kernel size. The water bodies area has decreased, which affected the overall accuracy of IoU = 0.870 (for comparison, on 22 July 2019 the Lee 5 × 5 filtering provides IoU = 0.876, with Lee 3 × 3 filtering IoU = 0.879).
4.2. “Water”/“Land” Class Imbalance
4.3. Ascending and Descending Orbits
4.4. Post-Processing
4.5. Recommendations and Future Research
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
AWEI | Automated Water Extraction Index |
BOA | Bottom of Atmosphere |
DEM | Digital Elevation Model |
FN | False Negative |
FP | False Positive |
GRD | Ground Range Detected |
HAND | Height Above Nearest Drainage |
IDE | Integrated Development Environment |
IoU | Intersection-Over-Union |
IW | Interferometric Wide |
MAP | Maximum A Posteriori |
MNDWI | Modified Normalized Difference Water Index |
NDVI | Normalized Difference Vegetation Index |
NDWI | Normalized Difference Water Index |
NIR | Near-infra-red |
PA | Producer accuracy |
SAR | Synthetic aperture radar |
SD | Standard deviation |
SWIR | Shortwave Infrared |
TP | True Positive |
UA | User Accuracy |
VH | Vertical transmit and horizontal receive |
VV | Vertical transmit and vertical receive |
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Sentinel-1 | Sentinel-2 | |||
---|---|---|---|---|
Date | Time | Date | Time | |
Path 153 | Path 160 | |||
28 August 2021 | 04:41:12 | 15:57:48 | 28 August 2021 | 10:02:50 |
23 July 2021 | 04:41:10 | 15:57:46 | 26 July 2021 | 09:52:55 |
29 June 2021 | 04:41:09 | 15:57:45 | 29 June 2021 | 10:02:50 |
11 June 2021 | 04:40:30 | 15:57:11 | 11 June 2021 | 09:52:53 |
30 May 2021 | 04:40:22 | 15:57:10 | 30 May 2021 | 10:02:49 |
12 May 2021 | 04:41:06 | 15:57:42 | 12 May 2021 | 09:52:50 |
02 September 2020 | 04:41:07 | 15:57:43 | 02 September 2020 | 10:02:53 |
15 August 2020 | 04:40:21 | 15:57:09 | 15 August 2020 | 09:52:55 |
16 June 2020 | 04:40:16 | 15:57:05 | 14 June 2020 | 10:02:55 |
23 May 2020 | 04:40:16 | 15:57:04 | 22 May 2020 | 09:52:57 |
28 July 2019 | 04:40:13 | 15:57:01 | 25 July 2019 | 10:02:57 |
22 July 2019 | 04:40:56 | 15:57:33 | 25 July 2019 | 10:02:57 |
23 May 2019 | 04:40:54 | 15:57:29 | 18 May 2019 | 09:52:55 |
15 July 2018 | 04:40:51 | 15:57:26 | 12 July 2018 | 09:50:30 |
03 July 2018 | 04:40:50 | 15:57:25 | 02 July 2018 | 09:50:30 |
28 May 2018 | 04:40:48 | 15:57:23 | 31 May 2018 | 10:00:23 |
Path 153 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
NDWI | MNDWI | ||||||||||
Sf 1 | IoU | Sf | IoU | Sf | IoU | Sf | IoU | Sf | IoU | Sf | IoU |
L7 2 | 0.879 | M7 3 | 0.876 | M7 | 0.882 | M7 | 0.908 | LS7 4 | 0.883 | M7 | 0.894 |
LS7 | 0.879 | LS7 | 0.874 | LS7 | 0.881 | LS7 | 0.906 | M7 | 0.882 | LS7 | 0.893 |
GM7 5 | 0.876 | LS5 | 0.869 | M5 | 0.881 | L7 | 0.905 | LS5 | 0.876 | LS5 | 0.890 |
M7 | 0.876 | M5 | 0.869 | LS5 | 0.880 | GM7 | 0.902 | M5 | 0.874 | M5 | 0.889 |
L5 | 0.871 | RL 6 | 0.861 | RL | 0.878 | L5 | 0.901 | L5 | 0.870 | L5 | 0.886 |
LS5 | 0.870 | L5 | 0.859 | M3 | 0.875 | LS5 | 0.901 | RL | 0.870 | L3 | 0.885 |
GM5 | 0.870 | GM5 | 0.857 | L3 | 0.875 | GM5 | 0.899 | GM5 | 0.869 | GM3 | 0.885 |
M5 | 0.865 | L7 | 0.853 | GM3 | 0.874 | M5 | 0.896 | GM7 | 0.867 | GM5 | 0.885 |
L3 | 0.846 | L3 | 0.851 | L5 | 0.874 | RL | 0.880 | L7 | 0.866 | GM7 | 0.882 |
GM3 | 0.845 | GM3 | 0.850 | GM5 | 0.872 | L3 | 0.879 | M3 | 0.865 | L7 | 0.881 |
RL | 0.845 | GM7 | 0.850 | L7 | 0.871 | GM3 | 0.878 | L3 | 0.864 | RL | 0.881 |
M3 | 0.837 | M3 | 0.849 | GM7 | 0.869 | M3 | 0.869 | GM3 | 0.863 | M3 | 0.880 |
None | 0.775 | None | 0.811 | None | 0.854 | None | 0.805 | None | 0.832 | None | 0.868 |
SD 7 | 0.029 | SD | 0.017 | SD | 0.007 | SD | 0.028 | SD | 0.013 | SD | 0.007 |
LS7 | 0.887 | M7 | 0.878 | LS7 | 0.887 | M7 | 0.905 | M7 | 0.890 | M7 | 0.897 |
L7 | 0.885 | LS7 | 0.877 | M7 | 0.887 | LS7 | 0.904 | LS7 | 0.888 | LS7 | 0.894 |
M7 | 0.885 | M5 | 0.876 | M5 | 0.886 | L7 | 0.903 | LS5 | 0.883 | LS5 | 0.892 |
L5 | 0.882 | LS5 | 0.876 | LS5 | 0.886 | GM7 | 0.900 | M5 | 0.882 | M5 | 0.892 |
GM7 | 0.881 | RL | 0.874 | RL | 0.884 | L5 | 0.900 | L5 | 0.879 | RL | 0.890 |
LS5 | 0.881 | L3 | 0.869 | M3 | 0.883 | LS5 | 0.900 | GM5 | 0.878 | L5 | 0.890 |
GM5 | 0.879 | GM3 | 0.869 | L3 | 0.882 | GM5 | 0.898 | RL | 0.876 | GM5 | 0.889 |
M5 | 0.876 | L5 | 0.868 | GM3 | 0.881 | M5 | 0.898 | L7 | 0.873 | GM7 | 0.887 |
L3 | 0.864 | M3 | 0.868 | L5 | 0.881 | RL | 0.892 | L3 | 0.872 | L7 | 0.887 |
GM3 | 0.864 | GM5 | 0.867 | GM5 | 0.880 | L3 | 0.889 | GM3 | 0.871 | GM3 | 0.886 |
RL | 0.860 | L7 | 0.864 | L7 | 0.879 | GM3 | 0.888 | GM7 | 0.871 | L3 | 0.886 |
M3 | 0.858 | GM7 | 0.861 | GM7 | 0.877 | M3 | 0.884 | M3 | 0.867 | M3 | 0.885 |
None | 0.821 | None | 0.828 | None | 0.868 | None | 0.835 | None | 0.836 | None | 0.874 |
SD | 0.018 | SD | 0.013 | SD | 0.005 | SD | 0.018 | SD | 0.013 | SD | 0.006 |
NDWI | MNDWI | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Filter | Start 1 | End 2 | PW 3 | IoU | Filter | Start | End | PW | IoU | ||
L7 4 | −14.8 | −19.3 | 4.5 | 0.854 | GM7 5 | −14.8 | −19.2 | 4.4 | 0.836 | ||
GM7 | −14.9 | −19.3 | 4.4 | 0.852 | L7 | −14.7 | −19.1 | 4.4 | 0.837 | ||
LS7 6 | −15.0 | −19.4 | 4.4 | 0.876 | LS7 | −14.9 | −19.2 | 4.3 | 0.866 | ||
GM7 | −17.1 | −21.5 | 4.4 | 0.869 | GM5 | −15.1 | −19.3 | 4.2 | 0.845 | ||
L7 | −17.1 | −21.5 | 4.4 | 0.870 | M7 7 | −15.3 | −19.5 | 4.2 | 0.871 | ||
LS7 | −17.3 | −21.7 | 4.4 | 0.885 | GM7 | −17.0 | −21.2 | 4.2 | 0.857 | ||
M7 | −15.4 | −19.7 | 4.3 | 0.878 | L5 | −15.1 | −19.2 | 4.1 | 0.846 | ||
M7 | −17.7 | −22.0 | 4.3 | 0.886 | LS5 | −15.1 | −19.2 | 4.1 | 0.861 | ||
GM5 | −15.2 | −19.4 | 4.2 | 0.860 | GM5 | −17.3 | −21.4 | 4.1 | 0.864 | ||
L5 | −15.2 | −19.4 | 4.2 | 0.862 | M7 | −17.5 | −21.6 | 4.1 | 0.886 | ||
GM5 | −17.4 | −21.6 | 4.2 | 0.875 | L5 | −17.3 | −21.4 | 4.1 | 0.865 | ||
L5 | −17.4 | −21.6 | 4.2 | 0.876 | L7 | −17.0 | −21.1 | 4.1 | 0.857 | ||
LS5 | −17.5 | −21.7 | 4.2 | 0.884 | LS7 | −17.2 | −21.3 | 4.1 | 0.880 | ||
LS5 | −15.3 | −19.4 | 4.1 | 0.873 | M5 | −15.5 | −19.5 | 4.0 | 0.867 | ||
M5 | −17.9 | −21.9 | 4.0 | 0.885 | M5 | −17.7 | −21.7 | 4.0 | 0.881 | ||
M5 | −15.7 | −19.6 | 3.9 | 0.875 | LS5 | −17.4 | −21.4 | 4.0 | 0.877 | ||
RL 8 | −15.7 | −19.5 | 3.8 | 0.874 | RL | −17.7 | −21.5 | 3.8 | 0.880 | ||
RL | −17.9 | −21.7 | 3.8 | 0.884 | RL | −15.6 | −19.3 | 3.7 | 0.865 | ||
GM3 | −15.7 | −19.3 | 3.6 | 0.864 | GM3 | −17.7 | −21.4 | 3.7 | 0.869 | ||
L3 | −15.7 | −19.3 | 3.6 | 0.865 | L3 | −17.7 | −21.4 | 3.7 | 0.870 | ||
GM3 | −17.9 | −21.5 | 3.6 | 0.878 | GM3 | −15.6 | −19.2 | 3.6 | 0.852 | ||
L3 | −17.9 | −21.5 | 3.6 | 0.879 | L3 | −15.6 | −19.2 | 3.6 | 0.852 | ||
M3 | −16.0 | −19.5 | 3.5 | 0.867 | M3 | −15.9 | −19.4 | 3.5 | 0.857 | ||
M3 | −18.2 | −21.7 | 3.5 | 0.881 | M3 | −18.0 | −21.5 | 3.5 | 0.876 | ||
L7 | −20.6 | −23.8 | 3.2 | 0.894 | GM7 | −20.2 | −23.3 | 3.1 | 0.919 | ||
G7 | −20.7 | −23.8 | 3.1 | 0.892 | L7 | −20.2 | −23.3 | 3.1 | 0.920 | ||
LS7 | −20.8 | −23.8 | 3.0 | 0.897 | LS7 | −20.3 | −23.4 | 3.1 | 0.924 | ||
M7 | −20.7 | −23.7 | 3.0 | 0.926 |
29 June 2021 1 | Otsu IoU | Reference Date—11 June 2021 | All Dates 2018–2020 | ||||
---|---|---|---|---|---|---|---|
Th 2 | Opt. IoU 3 | IoU 4 | Th | Opt. IoU | IoU | ||
Lee 5 × 5 | 0.911 | −22.5 | 0.896 | 0.015 | −22.7 | 0.913 | −0.002 |
Lee 7 × 7 | 0.916 | −22.2 | 0.911 | 0.005 | −22.5 | 0.917 | −0.001 |
Lee Sigma 5 × 5 | 0.911 | −22.4 | 0.890 | 0.021 | −22.7 | 0.914 | −0.003 |
Lee Sigma 7 × 7 | 0.914 | −22.4 | 0.902 | 0.012 | −22.7 | 0.920 | −0.006 |
Median 5 × 5 | 0.899 | −22.7 | 0.887 | 0.012 | −23.0 | 0.908 | −0.009 |
Median 7 × 7 | 0.902 | −23.0 | 0.899 | 0.003 | −23.1 | 0.919 | −0.017 |
28 August 2021 | OtsuIoU | Reference Date—11 June 2021 | All Dates 2018–2020 | ||||
Th | Opt.IoU | IoU | Th | Opt.IoU | IoU | ||
Lee 5 × 5 | 0.924 | −22.2 | 0.902 | 0.022 | −22.7 | 0.918 | 0.006 |
Lee 7 × 7 | 0.919 | −22.3 | 0.904 | 0.015 | −22.5 | 0.915 | 0.004 |
Lee Sigma 5 × 5 | 0.928 | −22.2 | 0.900 | 0.028 | −22.7 | 0.922 | 0.006 |
Lee Sigma 7 × 7 | 0.932 | −21.8 | 0.902 | 0.030 | −22.7 | 0.923 | 0.009 |
Median 5 × 5 | 0.928 | −22.3 | 0.901 | 0.027 | −22.7 | 0.922 | 0.006 |
Median 7 × 7 | 0.933 | −22.3 | 0.904 | 0.029 | −23.1 | 0.926 | 0.007 |
SAR Date | NDWI | MNDWI | ||||||
---|---|---|---|---|---|---|---|---|
Similarity (IoU) | Threshold (dB) | Similarity (IoU) | Threshold (dB) | |||||
153 1 | 160 1 | 153 | 160 | 153 | 160 | 153 | 160 | |
28 August 2021 | 0.880 | 0.884 | −22.7 | −24.6 | 0.926 | 0.936 | −21.3 | −22.7 |
23 July 2021 | 0.796 | 0.802 | −23.1 | −24.1 | 0.916 | 0.900 | −21.2 | −21.8 |
29 June 2021 | 0.831 | 0.841 | −23.1 | −23.9 | 0.918 | 0.907 | −22.2 | −22.2 |
11 June 2021 | 0.899 | 0.901 | −23.4 | −23.7 | 0.904 | 0.895 | −23.0 | −23.1 |
30 May 2021 | 0.744 | 0.749 | −23.9 | −24.8 | 0.891 | 0.892 | −23.6 | −24.0 |
12 May 2021 | 0.905 | 0.907 | −22.1 | −23.2 | 0.884 | 0.891 | −22.7 | −23.9 |
02 September 2020 | 0.880 | 0.885 | −23.2 | −24.3 | 0.881 | 0.883 | −22.7 | −23.3 |
15 August 2020 | 0.874 | 0.879 | −23.6 | −24.6 | 0.906 | 0.912 | −22.5 | −23.3 |
16 June 2020 | 0.909 | 0.914 | −23.0 | −23.7 | 0.906 | 0.912 | −22.9 | −23.5 |
23 May 2020 | 0.827 | 0.827 | −23.2 | −24.2 | 0.906 | 0.910 | −23.5 | −24.4 |
28 July 2019 | 0.887 | 0.894 | −22.6 | −23.1 | 0.896 | 0.906 | −22.1 | −22.4 |
22 July 2019 | 0.894 | 0.899 | −22.5 | −23.4 | 0.920 | 0.922 | −21.6 | −21.5 |
23 May 2019 | 0.853 | 0.858 | −23.2 | −24.7 | 0.873 | 0.883 | −23.3 | −24.8 |
15 July 2018 | 0.892 | 0.893 | −24.2 | −23.2 | 0.903 | 0.898 | −22.0 | −22.4 |
03 July 2018 | 0.878 | 0.887 | −22.7 | −24.0 | 0.881 | 0.885 | −21.8 | −22.8 |
28 May 2018 | 0.797 | 0.811 | −23.2 | −24.0 | 0.912 | 0.924 | −22.9 | −23.6 |
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Kavats, O.; Khramov, D.; Sergieieva, K. Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask. Water 2022, 14, 4030. https://doi.org/10.3390/w14244030
Kavats O, Khramov D, Sergieieva K. Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask. Water. 2022; 14(24):4030. https://doi.org/10.3390/w14244030
Chicago/Turabian StyleKavats, Olena, Dmitriy Khramov, and Kateryna Sergieieva. 2022. "Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask" Water 14, no. 24: 4030. https://doi.org/10.3390/w14244030
APA StyleKavats, O., Khramov, D., & Sergieieva, K. (2022). Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask. Water, 14(24), 4030. https://doi.org/10.3390/w14244030