An Object-Oriented Method for Extracting Single-Object Aquaculture Ponds from 10 m Resolution Sentinel-2 Images on Google Earth Engine
"> Figure 1
<p>World aquaculture and fishery production and consumption. Data source: [<a href="#B14-remotesensing-15-00856" class="html-bibr">14</a>].</p> "> Figure 2
<p>Study region selected in this study: (<b>a</b>) location of the study region; (<b>b</b>) overview of the study region; (<b>c</b>) Sentinel-2 true-color RSI; (<b>d</b>) detailed image from 0.5 m Hr-RSI, where SOAPs can be explicitly seen; (<b>e</b>) detailed image from 10 m Sentinel-2 Mr-RSI, where SOAPs are hard to distinguish. All maps in this paper are projected using the Cylindrical Equal Area project (ESRI: 54034).</p> "> Figure 3
<p>Framework of the proposed method for SOAPs extraction.</p> "> Figure 4
<p>Flowchart of the iterative algorithm combining grayscale morphology and Canny edge detection (Note: Y = Yes; N = No).</p> "> Figure 5
<p>Example of the use of the proposed iterative algorithm to segment water pixels: (<b>a</b>) 0.5 m Hr-RSI: (<b>b</b>) 10 m Mr-RSI from Sentinel-2: (<b>c</b>) maximum NDWI image (MNI); (<b>d</b>) Canny edge image (CEI) generated from the MNI at the first iteration, where a few aquaculture ponds are segmented completely; (<b>e</b>) CEI at the second iteration, where most aquaculture ponds are segmented completely; (<b>f</b>) CEI at the third iteration, where all the aquaculture ponds are segmented completely.</p> "> Figure 6
<p>Introduction of the segmentation degree detection method: (<b>a</b>) examples of over-segmented objects (LSI <math display="inline"><semantics> <mrow> <mo>></mo> <mn>2.5</mn> </mrow> </semantics></math> and RPOC <math display="inline"><semantics> <mo>≤</mo> </semantics></math> 1.5); (<b>b</b>) examples of appropriately segmented objects (LSI <math display="inline"><semantics> <mrow> <mo>≤</mo> <mn>2.5</mn> </mrow> </semantics></math> and RPOC <math display="inline"><semantics> <mo>≤</mo> </semantics></math> <math display="inline"><semantics> <mrow> <mn>1.5</mn> </mrow> </semantics></math>); (<b>c</b>) examples of under-segmented objects (LSI <math display="inline"><semantics> <mo>></mo> </semantics></math> <math display="inline"><semantics> <mrow> <mn>2.5</mn> </mrow> </semantics></math> and RPOC <math display="inline"><semantics> <mo>></mo> </semantics></math> <math display="inline"><semantics> <mrow> <mn>1.5</mn> </mrow> </semantics></math>); (<b>d</b>) examples of under-segmented objects (LSI <math display="inline"><semantics> <mo>≤</mo> </semantics></math> <math display="inline"><semantics> <mrow> <mn>2.5</mn> </mrow> </semantics></math> and RPOC <math display="inline"><semantics> <mo>></mo> </semantics></math> <math display="inline"><semantics> <mrow> <mn>1.5</mn> </mrow> </semantics></math>); (<b>e</b>) an illustration of these segmentation degrees.</p> "> Figure 7
<p>Decision tree for extracting aquaculture ponds from potential SOAPs (Note: Y = Yes; N = No).</p> "> Figure 8
<p>Extraction result of SOAPs in the study region: (<b>a</b>) a comprehensive overview of the distribution of aquaculture ponds in the study region; (<b>b</b>) the similar shapes and sizes of SOAPs extracted in this study compared to the ground-truth data; (<b>c</b>) most extracted aquaculture ponds were separated from adjacent waters and extracted as SOAPs; (<b>d</b>) abandoned ponds excluded from the extraction result.</p> "> Figure 9
<p>(<b>a</b>) Number of SOAPs in different areal ranges extracted in the study region and (<b>b</b>) histogram of the numerical distribution of SOAPs ranging in size from 0 to 10,000 <math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">m</mi> <mn>2</mn> </msup> </mrow> </semantics></math>.</p> "> Figure 10
<p>Comparison between extracted SOAPs and labeled SOAPs: (<b>a</b>) location of the verification region; (<b>b</b>) distribution of omission and commission SOAPs; (<b>c</b>) example of commission SOAPs; (<b>d</b>) example of omission SOAPs.</p> "> Figure 11
<p>Image segmentation result comparisons among the proposed, K-Means, G-Means, and SNIC methods; the red contours represent the labeled SOAPs.</p> "> Figure 12
<p>Segmentation accuracies comparison between the proposed K-Means, G-Means, and SNIC methods in different SOAP size classes: (<b>a</b>) RMSEs of the four methods for SOAPs in different size ranges; (<b>b</b>) as in (<b>a</b>), but for the MAEs; (<b>c</b>) as in (<b>a</b>), but for the MAPEs; (<b>d</b>) as in (<b>a</b>), but for the MIoUs.</p> ">
Abstract
:1. Introduction
2. Materials
2.1. Study Region
2.2. Data
3. Methodology
3.1. Water Pixels Identification
3.2. Water Segmentation and Selection
- Inputs: MNI and BWI.
- Output: potential SOAPs.
- Parameters: is the total number of iterations; is the sequence number during the iteration.
- Step 1: Set the value of to 0.
- Step 2: Compare the values of and . If , then go to Step 3; otherwise, end the procedure and output the potential SOAPs.
- Step 3: Use a 3 3 square kernel to implement the GM erosion operation on the MNI and output a processed MNI.
- Step 4: Implement the CED operation (threshold = 0.2) on the processed MNI and output a Canny edge image (CEI).
- Step 5: If i = 0, then go to Step 6; otherwise, overlay the CEI with the previously output CEIs and output an accumulated CEI.
- Step 6: For the BWI, remove the intersected pixels between the output CEI and the BWI and then output a segmented BWI with water segments.
- Step 7: Implement the connect component labeling operation on the segmented BWI and mark all water segments as unique water objects based on pixel connectivity (four-connected).
- Step 8: Implement segmentation degree detection on all water objects and select those passing this detection as potential SOAPs.
- Step 9: Remove the pixels belonging to potential SOAPs from the BWI and output a new BWI for Step 6.
- Step 10: Expand the boundaries of the newly acquired potential SOAPs and set the distance to expand these SOAPs equal to .
- Step 11: Set to , then go to Step 2.
3.2.1. Grayscale Morphology and Canny Edge Detection
3.2.2. Segmentation Degree Detection
3.3. Aquaculture Ponds Extraction
4. Results
4.1. Mapping Aquaculture Ponds
4.2. SOAPs Extraction Accuracy Assessment
4.2.1. Classification Accuracy Assessment
4.2.2. Segmentation Accuracy Assessment and Comparison
5. Discussion
5.1. A Transferable Approach
5.2. Future Work
6. Conclusions
- A total of 3577 aquaculture ponds were extracted in the study region, with a total area of 13,208,439.33 . Most aquaculture ponds were 0–10,000 in size, accounting for 96.39% of all SOAPs extracted in this study, indicating that the aquaculture industry in the study region is dominated by small-scale ponds.
- The proposed method could extract SOAPs with high accuracy. The relative error of the total areas between labeled SOAPs and extracted SOAPs was 1.13%, and the omission errors of labeled SOAPs were 3.46% in number and 1.95% in area, revealing that our method could effectively map aquaculture ponds.
- The proposed method showed better performance in segmenting SOAPs than K-Means, G-Means, and SNIC methods provided by GEE. The MIoU of our method was 0.6965, representing an improvement of between 0.1925 and 0.3268 over the comparative methods. The MIoUs of the proposed methods at all SOAP size classes were higher than those of the comparative methods, indicating that our method is superior to widely used image segmentation algorithms in segmenting SOAPs.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
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Band Name | Description | Spatial Resolution (m) | Wavelength (nm) |
---|---|---|---|
B2 | Blue | 10 | 496.6 (S2A)/492.1 (S2B) |
B3 | Green | 10 | 560 (S2A)/559 (S2B) |
B4 | Red | 10 | 664.5 (S2A)/665 (S2B) |
B8 | NIR 1 | 10 | 835.1 (S2A)/833 (S2B) |
B11 | SWIR 2 1 | 20 | 1613.7 (S2A)/1610.4 (S2B) |
B12 | SWIR 2 | 20 | 2202.4 (S2A)/2185.7 (S2B) |
QA60 3 | Cloud mask | 60 | —— |
SOAP Size | Number | Omission | Omission (%) | Omission % from Total Number | Omission Area (%) | Omission (%) from Total Area | ||
---|---|---|---|---|---|---|---|---|
All | 433 | 15 | 3.46 | 3.46 | 1,737,425.02 | 33,919.10 | 1.95 | 1.95 |
≤2000 | 63 | 7 | 11.11 | 1.62 | 91,731.57 | 10,979.46 | 11.97 | 0.63 |
2000–4000 | 202 | 8 | 3.96 | 1.85 | 603,841.49 | 22,939.64 | 3.80 | 1.32 |
4000–6000 | 100 | 0 | 0.00 | 0.00 | 485,161.17 | 0.00 | 0.00 | 0.00 |
6000–8000 | 47 | 0 | 0.00 | 0.00 | 324,497.41 | 0.00 | 0.00 | 0.00 |
8000–10,000 | 12 | 0 | 0.00 | 0.00 | 102,317.70 | 0.00 | 0.00 | 0.00 |
>10,000 | 9 | 0 | 0.00 | 0.00 | 129,875.68 | 0.00 | 0.00 | 0.00 |
SOAP Size | Number | Commission | Commission% | Commission % from Total Number | Area (m2) | Commission Area (m2) | Commission Area (%) | Commission (%) from Total Area |
---|---|---|---|---|---|---|---|---|
All | 526 | 94 | 17.87 | 17.87 | 17,57,058.66 | 231,476.63 | 13.17 | 13.17 |
≤2000 m2 | 171 | 52 | 30.41 | 9.89 | 236,285.85 | 64,936.18 | 27.48 | 3.70 |
2000–4000 | 208 | 26 | 12.50 | 4.94 | 595,886.07 | 75,470.61 | 12.67 | 4.30 |
4000–6000 | 90 | 12 | 13.33 | 2.28 | 433,611.42 | 60,642.55 | 13.99 | 3.45 |
6000–8000 | 37 | 2 | 5.41 | 0.38 | 252,144.04 | 13,836.04 | 5.49 | 0.79 |
8000–10,000 | 8 | 2 | 25.00 | 0.38 | 68,465.23 | 16,591.25 | 24.23 | 0.94 |
>10,000 | 12 | 0 | 0.00 | 0.00 | 170,666.05 | 0.00 | 0.00 | 0.00 |
Method | Parameters |
---|---|
Proposed method | Canny threshold = 0.2; LSI threshold = 2.5; RPOC threshold= 1.5; area threshold = 520,000; median NDWI threshold = 0.15; number threshold of near-neighbor objects = 3 |
K-Means | numClusters = 6; numIterations = 20; neighborhoodSize = 0; forceConvergence = false; uniqueLabels = true |
G-Means | numIterations = 10; pValue = 582; neighborhoodSize = 0; uniqueLabels = true |
SNIC | size = 5; compactness = 1; connectivity = 4 |
Method | MAPE (%) | MIoU | ||
---|---|---|---|---|
Proposed method | 3850.47 | 1286.04 | 34.23 | 0.6965 |
K-Means | 3907.93 | 2355.55 | 72.64 | 0.4326 |
G-Means | 3556.88 | 2533.89 | 63.70 | 0.3697 |
SNIC | 2610.18 | 1803.92 | 49.21 | 0.5040 |
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Li, B.; Gong, A.; Chen, Z.; Pan, X.; Li, L.; Li, J.; Bao, W. An Object-Oriented Method for Extracting Single-Object Aquaculture Ponds from 10 m Resolution Sentinel-2 Images on Google Earth Engine. Remote Sens. 2023, 15, 856. https://doi.org/10.3390/rs15030856
Li B, Gong A, Chen Z, Pan X, Li L, Li J, Bao W. An Object-Oriented Method for Extracting Single-Object Aquaculture Ponds from 10 m Resolution Sentinel-2 Images on Google Earth Engine. Remote Sensing. 2023; 15(3):856. https://doi.org/10.3390/rs15030856
Chicago/Turabian StyleLi, Boyi, Adu Gong, Zikun Chen, Xiang Pan, Lingling Li, Jinglin Li, and Wenxuan Bao. 2023. "An Object-Oriented Method for Extracting Single-Object Aquaculture Ponds from 10 m Resolution Sentinel-2 Images on Google Earth Engine" Remote Sensing 15, no. 3: 856. https://doi.org/10.3390/rs15030856
APA StyleLi, B., Gong, A., Chen, Z., Pan, X., Li, L., Li, J., & Bao, W. (2023). An Object-Oriented Method for Extracting Single-Object Aquaculture Ponds from 10 m Resolution Sentinel-2 Images on Google Earth Engine. Remote Sensing, 15(3), 856. https://doi.org/10.3390/rs15030856