Automated Extraction of Urban Water Bodies from ZY‐3 Multi‐Spectral Imagery
<p>Manually drawn referential imagery.</p> "> Figure 2
<p>Comparison of ground objects’ spectral curves before and after the atmospheric correction.</p> "> Figure 3
<p>Different water extraction results generated by NNDWI1, NNDWI2 and NNDWI, respectively.</p> "> Figure 4
<p>Diagram of dilation constraint.</p> "> Figure 5
<p>The spectral feature curves of the shadow-area pixels: (<b>a</b>–<b>e</b>) typical spectral curves of five types of pixels.</p> "> Figure 6
<p>The overall flowchart of AUWEM.</p> "> Figure 7
<p>Comparison of water extraction results of three algorithms in different experimental areas.</p> "> Figure 8
<p>Comparison of water classification results among different algorithms in local areas (a small area in yellow rectangular frame from the image of <a href="#water-09-00144-f008" class="html-fig">Figure 8</a>).</p> "> Figure 9
<p>A comparison of classification accuracy among different algorithms in five experimental areas. (<b>a</b>) water commission error; (<b>b</b>) Water omission error; (<b>c</b>) Water total error; (<b>d</b>) Water producer accuracy; (<b>e</b>) Water user accuracy; (<b>f</b>) Kappa coefficient.</p> "> Figure 10
<p>Process of acquiring water edge area for evaluation. Edge of the reference images are extracted and processed by morphological dilation to acquire the water edge for evaluation.</p> "> Figure 11
<p>Comparison of water edge detection accuracy among different algorithms in five experimental areas. (<b>a</b>) Commission Error; (<b>b</b>) Omission Error; (<b>c</b>) Accuracy of edge detection.</p> "> Figure 12
<p>Comparison of results of classifying pixels of spectrally contaminated water bodies among different algorithms. The yellow circle indicates an area clearly undetected by the NDWI algorithm. The water body in this area is eutrophicated with a lot of algal vegetation that affects its spectral information, making it hard for the NDWI to detect.</p> "> Figure 13
<p>Intersection operation results. (<b>a</b>) First experimental results; (<b>b</b>) Second experimental results; (<b>c</b>) Third experimental results; (<b>d</b>) Fourth experimental results.</p> "> Figure 14
<p>Comparison between intersection result and NDWI result.</p> "> Figure 15
<p>Shadow detection results generated when solely applying the model. (<b>a</b>) First experimental results; (<b>b</b>) Second experimental results.</p> "> Figure 16
<p>The shadow detection results generated when combining the model with the NNDWI extraction results. (<b>a</b>) First experimental results; (<b>b</b>) Second experimental results.</p> "> Figure 17
<p>The different index of pixels after the PCA transformation, NNDWI2 and NDWI, respectively.</p> "> Figure 18
<p>A comparison among changes of NDWI’s water extraction accuracy when the threshold changes. (<b>a</b>) Water extraction accuracy of Beijing; (<b>b</b>) Water extraction accuracy of Guangzhou; (<b>c</b>) Water extraction accuracy of Suzhou; (<b>d</b>) Water extraction accuracy of Wuhan_1; (<b>e</b>) Water extraction accuracy of Wuhan_2.</p> "> Figure 19
<p>The changes in NNDWI1 when the threshold changes and Band4 and NNDWI2 remain unchanged. (<b>a</b>) Water extraction accuracy of Beijing; (<b>b</b>) Water extraction accuracy of Guangzhou; (<b>c</b>) Water extraction accuracy of Suzhou; (<b>d</b>) Water extraction accuracy of Wuhan_1; (<b>e</b>) Water extraction accuracy of Wuhan_2.</p> "> Figure 20
<p>The changes in NNDWI2 when the threshold changes and Band4 and NNDWI1 remain unchanged. (<b>a</b>) Water extraction accuracy of Beijing; (<b>b</b>) Water extraction accuracy of Guangzhou; (<b>c</b>) Water extraction accuracy of Suzhou; (<b>d</b>) Water extraction accuracy of Wuhan_1; (<b>e</b>) Water extraction accuracy of Wuhan_2.</p> "> Figure 21
<p>The changes in Band4 when the threshold changes and NNDWI1 and NNDWI2 remain unchanged. (<b>a</b>) Water extraction accuracy of Beijing; (<b>b</b>) Water extraction accuracy of Guangzhou; (<b>c</b>) Water extraction accuracy of Suzhou; (<b>d</b>) Water extraction accuracy of Wuhan_1; (<b>e</b>) Water extraction accuracy of Wuhan_2.</p> "> Figure 22
<p>Comparison of intersection on Band4 under different thresholds as constraint. The yellow segmentation area is larger than the green one, so according to the results in the figure, after the intersection there will be water body or shadow objects that cover different areas and are to be detected.</p> ">
Abstract
:1. Introduction
2. Study Areas and Data
2.1. Study Areas
2.2. Experimental ZY3 Imagery and Its Corresponding Reference Imagery
- Delineate precision of the fuzzy boundary of water body is within three pixels while the clear boundary of water body is within one pixels.
- Less than or equal to one pixels of water body information is not given to delineate.
- We choose reference of higher resolution Google map image in order to distinguish between water body and building shadow as well as the seemingly water body and non-water body.
- Urban water system is basically interconnected with each, other except for the river intercepted by bridge.
3. Method
3.1. Satellite Image Preprocessing
3.2. Normalized Difference Water Index (NDWI)
3.3. New Normalized Difference Water Indexes (NNDWI)
- Use the ZY-3 Blue band (Band1) to replace the green band in Equation (1) to obtain NNDWI1, i.e.,
- Four bands of ZY-3 imagery were processed by the Principal Component Analysis (PCA) transformation [41], use the first principle component after PCA transformation to replace the Green band in Equation (1) to obtain NNDWI2, i.e.,
3.4. Shadow Detection Based on Object Oriented Technology
3.4.1. Shadow Objects
3.4.2. The Shadow Objects Description (The Description of Spectral Feature Relations between Water-Body Pixels and Shadow-Area Pixels)
3.4.3. The Shadow Objects Detection Method
3.5. Urban Water Extraction and Its Accuracy Evaluation
4. Experimental Results and Analysis
4.1. Water Extraction Maps
4.2. Water Extraction Accuracy
4.3. An Analysis of Water-Edge Pixel Extraction Accuracy
- Use the reference image to acquire the water edge by applying the Canny operator.
- Apply the morphological dilation to the acquired edge to establish a buffer zone centered around the edge with a radius of four pixels.
- Determine the pixels in the buffer zone. Suppose that the total number of pixels in the buffer zone is N, the number of correctly classified pixels is NR, the number of omitted pixels is No, and the number of commission error is Nc, then:
5. Discussion
5.1. Effect of PCA Transformation
5.2. Effect of Intersection
5.3. Shadow Detection Ability of the Shadow Object Description Method
5.4. Threshold Setting and Stability of Algorithm in Correlation Computation
5.5. Summary
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 34,961 | 11,657 | 46,618 | 74.9946 | 25.0054 |
No_water | 1061 | 2,244,771 | 2,245,832 | 99.9528 | 0.0472 |
Total | 36,022 | 2,256,428 | 2,292,450 | ||
User Accuracy (%) | 97.0546 | 99.4834 | |||
Commission Error (%) | 2.9454 | 0.5166 | |||
Overall Accuracy = 99.4452%; Kappa Coefficient = 84.3326% |
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 34,827 | 11,791 | 46,618 | 74.7072 | 25.2928 |
No_water | 2125 | 2,243,707 | 2,245,832 | 99.9054 | 0.0946 |
Total | 36,952 | 2,255,498 | 2,292,450 | ||
User Accuracy (%) | 94.2493 | 99.4772 | |||
Commission Error (%) | 5.7507 | 0.5228 | |||
Overall Accuracy = 99.3930%; Kappa Coefficient = 83.0431% |
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 40,929 | 5689 | 46,618 | 87.7966 | 12.2034 |
No_water | 1571 | 2,244,261 | 2,245,832 | 99.9300 | 0.0700 |
Total | 42,500 | 2,249,950 | 2,292,450 | ||
User Accuracy (%) | 96.3035 | 99.7471 | |||
Commission Error (%) | 3.6965 | 0.2529 | |||
Overall accuracy = 99.6833%; Kappa Coefficient = 91.6924% |
Appendix B
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 1,212,617 | 169,976 | 1,382,593 | 87.7060 | 12.2940 |
No_water | 19,157 | 8,988,885 | 9,008,042 | 99.7873 | 0.2127 |
Total | 1,231,774 | 9,158,861 | 10,390,635 | ||
User Accuracy (%) | 98.4448 | 98.1441 | |||
Commission Error (%) | 1.5552 | 1.8559 | |||
Overall accuracy = 98.1798%; Kappa Coefficient = 91.7285% |
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 1,087,494 | 295,099 | 1,382,593 | 78.6561 | 21.3439 |
No_water | 29,105 | 8,978,937 | 9,008,042 | 99.6769 | 0.3231 |
Total | 1,116,599 | 9,274,036 | 10,390,635 | ||
User Accuracy (%) | 97.3934 | 96.8180 | |||
Commission Error (%) | 2.6066 | 3.1820 | |||
Overall accuracy = 96.8798%; Kappa Coefficient = 85.2771% |
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 1,304,001 | 78,592 | 1,382,593 | 94.3156 | 5.6844 |
No_water | 26,733 | 8,981,309 | 9,008,042 | 99.7032 | 0.2968 |
Total | 1,330,734 | 9,059,901 | 10,390,635 | ||
User Accuracy (%) | 97.9911 | 99.1325 | |||
Commission Error (%) | 2.0089 | 0.8675 | |||
Overall accuracy = 98.9863%; Kappa Coefficient = 95.5355% |
Appendix C
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 415,717 | 76,225 | 491,942 | 84.5053 | 15.4947 |
No_water | 50,948 | 5,673,154 | 5,724,102 | 99.1099 | 0.8901 |
Total | 466,665 | 5,749,379 | 6,216,044 | ||
User Accuracy (%) | 89.0825 | 98.6742 | |||
Commission Error (%) | 10.9175 | 1.3258 | |||
Overall accuracy = 97.9541%; Kappa Coefficient = 85.6260% |
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 420,726 | 71,216 | 491,942 | 85.5235 | 14.4765 |
No_water | 130,884 | 5,593,218 | 5,724,102 | 97.7135 | 2.2865 |
Total | 551,610 | 5,664,434 | 6,216,044 | ||
User Accuracy (%) | 76.2724 | 98.7428 | |||
Commission Error (%) | 23.7276 | 1.2572 | |||
Overall accuracy = 96.7487%; Kappa Coefficient = 78.8652% |
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 429,101 | 62,841 | 491,942 | 87.2259 | 12.7741 |
No_water | 45,182 | 5,678,920 | 5,724,102 | 99.2107 | 0.7893 |
Total | 474,283 | 5,741,761 | 6,216,044 | ||
User Accuracy (%) | 90.4736 | 98.9055 | |||
Commission Error (%) | 9.5264 | 1.0945 | |||
Overall accuracy = 98.2622%Kappa Coefficient = 87.8783% |
Appendix D
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 1,562,974 | 182,267 | 1,745,241 | 89.5563 | 10.4437 |
No_water | 9274 | 3,905,130 | 3,914,404 | 99.7631 | 0.2369 |
Total | 1,572,248 | 4,087,397 | 5,659,645 | ||
User Accuracy (%) | 99.4101 | 95.5408 | |||
Commission Error (%) | 0.5899 | 4.4592 | |||
Overall accuracy = 96.6157%; Kappa Coefficient = 91.8418% |
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 1,526,202 | 219,039 | 1,745,241 | 87.4494 | 12.5506 |
No_water | 146,867 | 3,767,537 | 3,914,404 | 96.2480 | 5.4944 |
Total | 1,673,069 | 3,986,576 | 5,659,645 | ||
User Accuracy (%) | 91.2217 | 94.5056 | |||
Commission Error (%) | 8.7783 | 3.7520 | |||
Overall accuracy = 93.5348%; Kappa Coefficient = 84.6675% |
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 1,676,387 | 68,854 | 1,745,241 | 96.0548 | 3.9452 |
No_water | 17,803 | 3,896,601 | 3,914,404 | 99.5452 | 0.4548 |
Total | 1,694,190 | 3,965,455 | 5,659,645 | ||
User Accuracy (%) | 98.9492 | 98.2637 | |||
Commission Error (%) | 1.0508 | 1.7363 | |||
Overall accuracy = 98.4689%; Kappa Coefficient = 96.3811% |
Appendix E
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 2,084,870 | 303,303 | 2,388,173 | 87.2998 | 12.7002 |
No_water | 17,198 | 7,422,653 | 7,439,851 | 99.7688 | 0.2312 |
Total | 2,102,068 | 7,725,956 | 9,828,024 | ||
User Accuracy (%) | 99.1819 | 96.0742 | |||
Commission Error (%) | 0.7201 | 3.9258 | |||
Overall accuracy = 96.7389%; Kappa Coefficient = 90.7601% |
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 2,114,412 | 273,761 | 2,388,173 | 88.5368 | 11.4632 |
No_water | 68,478 | 7,371,373 | 7,439,851 | 99.0796 | 0.9204 |
Total | 2,182,890 | 7,645,134 | 9,828,024 | ||
User Accuracy (%) | 96.8630 | 96.4191 | |||
Commission Error (%) | 3.1370 | 3.5809 | |||
Overall accuracy = 96.5177%; Kappa Coefficient = 90.2501% |
Ground Truth (Pixels) | |||||
---|---|---|---|---|---|
Class | Water | No_Water | Total | Produc Accuracy (%) | Omission Error (%) |
Water | 2,207,784 | 180,389 | 2,388,173 | 92.4466 | 7.5534 |
No_water | 41,320 | 7,398,531 | 7,439,851 | 99.4446 | 0.5554 |
Total | 2,249,104 | 7,578,920 | 9,828,024 | ||
User Accuracy (%) | 98.1628 | 97.6199 | |||
Commission Error (%) | 1.8372 | 2.3801 | |||
Overall accuracy = 97.7441%; Kappa Coefficient = 93.7445% |
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City’s Name and Location | Area Coverage (Pixels) | Water Body Type | Topography | Climate | Color Infrared Composite (4/3/2 Band Combination) |
---|---|---|---|---|---|
Beijing (39.9° N, 116.3° E) | 1479 × 1550 (77.1 km2) | Rivers Polluted lakes Clear lake | Plain | Warm temperate semi humid continental monsoon climate | |
Guangzhou (23° N, 113.6° E) | 2351 × 2644 (209.1 km2) | Rivers Ponds Polluted lakes Clear lake | Basin, plain | Typical monsoon climate in South Asia | |
Suzhou (31.2° N, 120.5° E) | 2351 × 2644 (209.1 km2) | Rivers Ponds Polluted lakes Clear lake | Basin, plain, hills. | Subtropical humid monsoon climate | |
Wuhan_1 (30.5° N, 114.3° E) | 2245 × 2521 (190.4 km2) | Rivers Ponds Large polluted lakes Large clear lakes | Basin, plain, hills. | Subtropical humid monsoon climate | |
Wuhan_2 (30.5° N, 114.3° E) | 2894 × 3396 (330.6 km2) | Rivers Ponds Large polluted lakes Large clear lakes | Basin, plain, hills. | Subtropical humid monsoon climate |
Item | Contents |
---|---|
Camera model | Panchromatic orthographic; Panchromatic front-view and rear-view; multi-spectral orthographic |
Resolution | Sub-satellite points full-color: 2.1 m; front- and rear-view 22° full color: 3.6 m; sub-satellite points multi-spectral: 5.8 m |
Wavelength | Panchromatic: 450 nm–800 nm Multi-spectral: Band1 (450 nm–520 nm); Band2 (520 nm–590 nm) Band3 (630 nm–690 nm); Band4 (770 nm–890 nm) |
Width | Sub-satellite points Panchromatic: 50 km, single-view 2500 km2; Sub-satellite points multi-spectral: 52 km, single-view 2704 km2 |
Revisit cycle | 5 days |
Daily image acquisition | Panchromatic: nearly 1,000,000 km2/day; Fusion: nearly 1,000,000 km2/day |
Test Site | ZY-3 Scenes | ||
---|---|---|---|
Acquisition Date | Path | Row | |
Beijing | 28 November 2013 | 002 | 125 |
Guangzhou | 20 October 2013 | 895 | 167 |
Suzhou | 17 December 2015 | 882 | 147 |
Wuhan_1 | 24 July 2016 | 001 | 149 |
Wuhan_2 | 28 March 2016 | 897 | 148 |
Method | Threshold | ||||
---|---|---|---|---|---|
Beijing | Guangzhou | Suzhou | Wuhan_1 | Wuhan_2 | |
AUWEM | T1 = 0, T2 = 0, T3 = 38 | T1 = 0, T2 = 0, T3 = 20 | T1 = 0, T2 = 0, T3 = 25 | T1 = 0, T2 = 0, T3 = 45 | T1 = 0, T2 = 0, T3 = 65 |
NDWI | T = −0.04 | T = −0.07 | T = 0.07 | T = 0.08 | T = 0.02 |
MaxLike | - | - | - | - | - |
Classification Algorithm | Beijing (1479 × 1550) | Guangzhou (2973 × 3495) | Suzhou (2351 × 2644) | Wuhan_1 (2245 × 2521) | Wuhan_2 (2894 × 3396) |
---|---|---|---|---|---|
Kappa (%) | Kappa (%) | Kappa (%) | Kappa (%) | Kappa (%) | |
AUWEM | 91.6924 | 95.5355 | 87.8783 | 96.3811 | 93.7445 |
NDWI | 83.0431 | 85.2771 | 78.8652 | 84.6675 | 90.2501 |
MaxLike | 84.3326 | 91.7285 | 85.6260 | 91.8418 | 90.7601 |
Site | Method | Commission Error (%) | Omission Error (%) | A (%) |
---|---|---|---|---|
Beijing | AUWEM | 1.8032 | 15.8446 | 82.3522 |
NDWI | 0.2042 | 29.9648 | 69.8310 | |
MaxLike | 0.0738 | 29.9747 | 69.9515 | |
Guangzhou | AUWEM | 0.3417 | 5.8892 | 93.7691 |
NDWI | 0.1438 | 21.4114 | 78.4448 | |
MaxLike | 0.0833 | 14.1019 | 85.8149 | |
Suzhou | AUWEM | 2.3455 | 12.5791 | 85.0755 |
NDWI | 2.2140 | 13.6943 | 84.0917 | |
MaxLike | 0.9649 | 14.2155 | 84.8196 | |
Wuhan_1 | AUWEM | 0.6422 | 9.8925 | 89.4653 |
NDWI | 0.9452 | 27.8494 | 71.2054 | |
MaxLike | 0.0211 | 26.3919 | 73.5870 | |
Wuhan_2 | AUWEM | 1.3827 | 19.0375 | 79.5798 |
NDWI | 0.4743 | 27.8402 | 71.6855 | |
MaxLike | 0.0335 | 30.1691 | 69.7974 |
Image Name | Image Size | Nb | Na | Nb-Na |
---|---|---|---|---|
a | 361 × 361 | 11,883 | 15,888 | 4005 |
b | 327 × 335 | 12,336 | 17,630 | 5294 |
c | 299 × 319 | 9923 | 12,218 | 2295 |
d | 677 × 762 | 76,932 | 57,389 | −19,543 |
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Yang, F.; Guo, J.; Tan, H.; Wang, J. Automated Extraction of Urban Water Bodies from ZY‐3 Multi‐Spectral Imagery. Water 2017, 9, 144. https://doi.org/10.3390/w9020144
Yang F, Guo J, Tan H, Wang J. Automated Extraction of Urban Water Bodies from ZY‐3 Multi‐Spectral Imagery. Water. 2017; 9(2):144. https://doi.org/10.3390/w9020144
Chicago/Turabian StyleYang, Fan, Jianhua Guo, Hai Tan, and Jingxue Wang. 2017. "Automated Extraction of Urban Water Bodies from ZY‐3 Multi‐Spectral Imagery" Water 9, no. 2: 144. https://doi.org/10.3390/w9020144
APA StyleYang, F., Guo, J., Tan, H., & Wang, J. (2017). Automated Extraction of Urban Water Bodies from ZY‐3 Multi‐Spectral Imagery. Water, 9(2), 144. https://doi.org/10.3390/w9020144