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Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

Postal Subscription Code 80-963

2018 Impact Factor: 1.205

Front. Earth Sci.    2019, Vol. 13 Issue (3) : 478-494    https://doi.org/10.1007/s11707-018-0702-5
RESEARCH ARTICLE
Automatic sub-pixel coastline extraction based on spectral mixture analysis using EO-1 Hyperion data
Zhonghua HONG1,2(), Xuesu LI1, Yanling HAN1(), Yun ZHANG1, Jing WANG1, Ruyan ZHOU1, Kening HU1
1. College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
2. Key Laboratory of Fisheries Information, Ministry of Agriculture, Shanghai 201306, China
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Abstract

Many megacities (such as Shanghai) are located in coastal areas, therefore, coastline monitoring is critical for urban security and urban development sustainability. A shoreline is defined as the intersection between coastal land and a water surface and features seawater edge movements as tides rise and fall. Remote sensing techniques have increasingly been used for coastline extraction; however, traditional hard classification methods are performed only at the pixel-level and extracting sub-pixel accuracy using soft classification methods is both challenging and time consuming due to the complex features in coastal regions. This paper presents an automatic sub-pixel coastline extraction method (ASPCE) from high-spectral satellite imaging that performs coastline extraction based on spectral mixture analysis and, thus, achieves higher accuracy. The ASPCE method consists of three main components: 1) A Water-Vegetation-Impervious-Soil (W-V-I-S) model is first presented to detect mixed W-V-I-S pixels and determine the endmember spectra in coastal regions; 2) The linear spectral mixture unmixing technique based on Fully Constrained Least Squares (FCLS) is applied to the mixed W-V-I-S pixels to estimate seawater abundance; and 3) The spatial attraction model is used to extract the coastline. We tested this new method using EO-1 images from three coastal regions in China: the South China Sea, the East China Sea, and the Bohai Sea. The results showed that the method is accurate and robust. Root mean square error (RMSE) was utilized to evaluate the accuracy by calculating the distance differences between the extracted coastline and the digitized coastline. The classifier’s performance was compared with that of the Multiple Endmember Spectral Mixture Analysis (MESMA), Mixture Tuned Matched Filtering (MTMF), Sequential Maximum Angle Convex Cone (SMACC), Constrained Energy Minimization (CEM), and one classical Normalized Difference Water Index (NDWI). The results from the three test sites indicated that the proposed ASPCE method extracted coastlines more efficiently than did the compared methods, and its coastline extraction accuracy corresponded closely to the digitized coastline, with 0.39 pixels, 0.40 pixels, and 0.35 pixels in the three test regions, showing that the ASPCE method achieves an accuracy below 12.0 m (0.40 pixels). Moreover, in the quantitative accuracy assessment for the three test sites, the ASPCE method shows the best performance in coastline extraction, achieving a 0.35 pixel-level at the Bohai Sea, China test site. Therefore, the proposed ASPCE method can extract coastline more accurately than can the hard classification methods or other spectral unmixing methods.

Keywords coastline      fully constrained least squares      spatial attraction algorithm      urban development      EO-1 data     
Corresponding Author(s): Zhonghua HONG,Yanling HAN   
Just Accepted Date: 24 April 2018   Online First Date: 07 June 2018    Issue Date: 15 October 2019
 Cite this article:   
Zhonghua HONG,Xuesu LI,Yanling HAN, et al. Automatic sub-pixel coastline extraction based on spectral mixture analysis using EO-1 Hyperion data[J]. Front. Earth Sci., 2019, 13(3): 478-494.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0702-5
https://academic.hep.com.cn/fesci/EN/Y2019/V13/I3/478
Fig.1  False-colour composite (RGB: bands 29, 20, 12) EO-1 Hyperion data for the three study areas: (a) South China Sea; (b) East China Sea; and (c) Bohai Sea, China.
Study area Coastal types Acquisition date of EO-1 data Acquisition date of reference data
South China Sea Sandy December 21, 2006 Google EarthTM image acquired on January 30, 2007
East China Sea Mud-deposition November 8, 2006 Google EarthTM image acquired on November 12, 2006
Bohai Sea, China Rock Estuary November 11, 2006 Google EarthTM image acquired on December 31, 2006
Tab.1  Details of the three study areas
Fig.2  General framework based on an automatic sub-pixel coastline extraction (ASPCE) method.
Four index names Abbreviation and definition
Normalized Difference Water Index (McFeeters, 1996) NDWI=(ρ(Green)ρ(NIR) )/( ρ(Green)+ρ (NIR))
Normalized Difference Vegetation Index (Santos and Negri, 1997) NDVI=(ρ(NIR)ρ (R))/(ρ(NIR)+ρ(R))
Normalized Difference Built-Up Index (Zha et al., 2003) NDBI=(ρ(SWIR)ρ(NIR) )/( ρ(SWIR)+ρ (NIR))
Normalized Difference Soil Index (Wolf, 2012) NDSI=(ρ(Green)ρ(Yellow))/(ρ(Green)+ρ( Yellow))
Tab.2  Calculations for the NDWI, NDVI, NDBI, and NDSI indexes for the extraction of seawater, vegetation, impervious surfaces, and soil, respectively
Test sites Discriminant criteria
South China Sea |tan?α|Non-water ,left 0.5, | tan?α|water,right1.732
East China Sea |tan?α|Non-water ,left 1.732, | tan?α|water,right1.732
Bohai Sea, China |tan?α|Non-water ,left 0.5, | tan?α|water,right1.732
Tab.3  Discriminant criteria for mixed W-V-I-S Pixel Extraction in the South China Sea, East China Sea, and Bohai Sea, China
Fig.3  Histograms of the NDWI in the (a) South China Sea, (b) East China Sea and (c) Bohai Sea, China test sites.
Fig.4  Extracted spectral curves of ground objects from the PPI algorithm and the reference spectra from four different indexes at (a) South China Sea; (b) East China Sea; and (c) Bohai Sea, China.
Spectrum vector Spectral angle/rad
Class A1 Class B1 Class C1 Class D1
Reference seawater 0.1142 0.9771 0.7160 0.4476
Reference vegetation 0.9768 0.0364 0.3625 0.5821
Reference impervious 0.6563 0.3372 0.0508 0.2678
Reference soil 0.3889 0.5726 0.3161 0.0556
Tab.4  The angle between the endmember spectrum vector and the reference spectrum vector at the South China Sea test site
Spectrum vector Spectral angle/rad
Class A2 Class E Class B2 Class C2 Class D2
Reference seawater 0.0660 0.0789 0.8104 0.4422 0.3288
Reference land water 0.0962 0.0670 0.8018 0.4245 0.3031
Reference vegetation 0.8027 0.7477 0.0220 0.3754 0.4995
Reference impervious 0.4811 0.4263 0.3528 0.0585 0.1663
Reference soil 0.3110 0.2584 0.5236 0.1465 0.0350
Tab.5  The angle between the endmember spectrum vector and the reference spectrum vector at the East China Sea test site
Spectrum vector Spectral angle/rad
Class A3 Class B3 Class C3 Class D3
Reference seawater 0.0795 1.0095 0.6876 0.4058
Reference vegetation 0.9667 0.0541 0.3654 0.6331
Reference impervious 0.6011 0.3744 0.0939 0.2685
Reference soil 0.3870 0.5810 0.2699 0.0576
Tab.6  The angle between the endmember spectrum vector and the reference spectrum vector at the Bohai Sea, China test site
Fig.5  Coastline extraction results based on the ASPCE method at the South China Sea test site. (a) Original image; (b) sub-pixel mapping result with each different endmember; (c) coastline extraction based on ASPCE method; (d) partial magnification of (c).
Fig.6  Coastline extraction results based on the ASPCE method at the East China Sea test site. (a) Original image; (b) sub-pixel mapping result with each different endmember; (c) coastline extraction based on ASPCE method; (d) partial magnification of (c).
Fig.7  Coastline extraction results based on the ASPCE method at the Bohai Sea, China test site. (a) Original image; (b) sub-pixel mapping result with each different endmember; (c) coastline extraction based on ASPCE method; (d) partial magnification of (c).
Fig.8  Comparison of the coastline extraction results. First row, (a)–(g) are, respectively, the digital coastline and coastline extractions from the proposed method and the compared methods (MESMA, MTMF, SMACC, CEM, and NDWI) in the South China Sea; Second row, (h)–(n) are, respectively, the digital coastline and coastline extractions from the proposed method and the compared methods (MESMA, MTMF, SMACC, CEM, and NDWI) in the East China Sea; Third row, (o)–(u) are, respectively, the digital coastline and coastline extractions from the proposed method and the compared methods (MESMA, MTMF, SMACC, CEM, and NDWI) in the Bohai Sea, China.
Fig.9  Detail results of the coastline extracted by seven methods overlaid on the original image at the (a) South China Sea, (b) East China Sea, and (c) Bohai Sea, China, respectively.
Fig.10  RMSE value of extracted coastlines with the proposed ASPCE, MESMA, MTMF, SMACC, CEM, and NDWI methods using different section spacing at the (a) South China Sea test site; (b) East China Sea test site; (c) Bohai Sea, China test site.
Fig.11  RMSE values of extracted coastlines by the different methods at 30 m section spacing in the Bohai Sea, China, East China Sea, and South China Sea sites.
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