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Water 2017, 9(2), 144; doi:10.3390/w9020144

Automated Extraction of Urban Water Bodies from ZY‐3 Multi‐Spectral Imagery

1
School of Geomatics, Liaoning Technical University, Fuxin 123000, China
2
Satellite Surveying and Mapping Application Center, National Administration of Surveying, Mapping and Geoinformation, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Academic Editors: Hongjie Xie and Xianwei Wang 
Received: 31 October 2016 / Accepted: 14 February 2017 / Published: 21 February 2017

Abstract

The extraction of urban water bodies from high‐resolution remote sensing images, which has been a hotspot in researches, has drawn a lot of attention both domestic and abroad. A challenging issue is to distinguish the shadow of high‐rise buildings from water bodies. To tackle this issue, we propose the automatic urban water extraction method (AUWEM) to extract urban water bodies from high‐resolution remote sensing images. First, in order to improve the extraction accuracy, we refine the NDWI algorithm. Instead of Band2 in NDWI, we select the first principal component after PCA transformation as well as Band1 for ZY‐3 multi‐spectral image data to construct two new indices, namely NNDWI1, which is sensitive to turbid water, and NNDWI2, which is sensitive to the water body whose spectral information is interfered by vegetation. We superimpose the image threshold segmentation results generated by applying NNDWI1 and NNDWI2, then detect and remove the shadows in the small areas of the segmentation results using object‐oriented shadow detection technology, and finally obtain the results of the urban water extraction. By comparing the Maximum Likelihood Method (MaxLike) and NDWI, we find that the average Kappa coefficients of AUWEM, NDWI and MaxLike in the five experimental areas are about 93%, 86.2% and 88.6%, respectively. AUWEM exhibits lower omission error rates and commission error rates compared with the NDWI and MaxLike. The average total error rates of the three methods are about 11.9%, 18.2%, and 22.1%, respectively. AUWEM not only shows higher water edge detection accuracy, but it also is relatively stable with the change of threshold. Therefore, it can satisfy demands of extracting water bodies from ZY‐3 images. View Full-Text
Keywords: ZY‐3 images; urban water bodies; automatic water extraction; NDWI; PCA transformation;  shadow detection ZY‐3 images; urban water bodies; automatic water extraction; NDWI; PCA transformation;  shadow detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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