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Frontiers of Earth Science

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 Authors: 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:  
http://journal.hep.com.cn/fesci/EN/10.1007/s11707-018-0702-5
http://journal.hep.com.cn/fesci/EN/Y2019/V13/I3/478
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Zhonghua HONG
Xuesu LI
Yanling HAN
Yun ZHANG
Jing WANG
Ruyan ZHOU
Kening HU
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.
1 M Alonzo, B Bookhagen, D A Roberts (2014). Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sens Environ, 148(148): 70–83
https://doi.org/10.1016/j.rse.2014.03.018
2 P M Atkinson (1997). Mapping Sub-Pixel Boundaries from Remotely Sensed Images. In: Kemp Z, ed. Innovations in GIS 4. London: Taylor and Francis: 166–180
3 P Barry (2001). EO-1 Hyperion Science Data User’s Guide, Level 1_B.TRW Space. Defense and Information Systems, 555–557
4 E C F Bird (1985). Coastline Changes. A global review. New York: John Wiley and Sons Inc.,108
5 E H Boak, I L Turner (2005). Shoreline definition and detection: a review. J Coast Res, 21(4): 688–703
https://doi.org/10.2112/03-0071.1
6 M Bouchahma, W Yan (2014). Monitoring shoreline change on Djerba Island using GIS and multi-temporal satellite data. Arab J Geosci, 7(9): 3705–3713
https://doi.org/10.1007/s12517-013-1052-9
7 E J M Delhez, A Barth (2011). Science based management of coastal waters. J Mar Syst, 88(1): 1–2
https://doi.org/10.1016/j.jmarsys.2011.02.007
8 K Di, J Wang, R Ma, R Li (2003). Automatic shoreline extraction from high resolution IKONOS satellite imagery. Cortex, 49(1): 184–199
9 Y Feng, Y Liu, D Liu (2015). Shoreline mapping with cellular automata and the shoreline progradation analysis in Shanghai, China from 1979 to 2008. Arab J Geosci, 8(7): 4337–4351
https://doi.org/10.1007/s12517-014-1515-7
10 G L Feyisa, H Meilby, R Fensholt, S R Proud (2014). Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sens Environ, 140(1): 23–35
https://doi.org/10.1016/j.rse.2013.08.029
11 G M Foody (1996). Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data. Int J Remote Sens, 17(7): 1317–1340
https://doi.org/10.1080/01431169608948706
12 G M Foody, A M Muslim, P M Atkinson (2003). Super-resolution Mapping of the Shoreline through Soft Classification Analyses. IEEE International Geoscience and Remote Sensing Symposium, (6): 3429–3431
13 J Franke, D A Roberts, K Halligan, G Menz (2009). Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sens Environ, 113(8): 1712–1723
https://doi.org/10.1016/j.rse.2009.03.018
14 P S Frazier, K J Page (2000). Water body detection and delineation with Landsat TM data. Photogramm Eng Remote Sensing, 66(12): 1461–1468
15 R Gens (2010). Remote sensing of coastlines: detection, extraction and monitoring. Int J Remote Sens, 31(7): 1819–1836
https://doi.org/10.1080/01431160902926673
16 İ Güneralp, A M Filippi, B U Hales (2013). River-flow boundary delineation from digital aerial photography and ancillary images using Support Vector Machines. GIsci Remote Sens, 50(1): 1–25
17 A T Harris (2006). Spectral mapping tools from the earth sciences applied to spectral microscopy data. Cytometry A, 69A(8): 872–879
https://doi.org/10.1002/cyto.a.20309
18 N Keshava, J F Mustard (2002). Spectral unmixing. IEEE Signal Process Mag, 19(1): 44–57
https://doi.org/10.1109/79.974727
19 J S Lee, I Jurkevich (1990). Coastline detection and tracing In SAR images. IEEE Trans Geosci Remote Sens, 28(4): 662–668
https://doi.org/10.1109/TGRS.1990.572976
20 R Li, K Di, R Ma (2003). 3-D shoreline extraction from IKONOS satellite imagery. Mar Geod, 26(1–2): 107–115
https://doi.org/10.1080/01490410306699
21 R Li, C W Keong, E Ramcharan, E Kjerfve, D Willis (1998). A coastal GIS for shoreline monitoring and management-case study in Malaysia. Surveying and Land Information Systems, 58(3): 157–166
22 W Li, P Gong (2016). Continuous monitoring of coastline dynamics in western Florida with a 30-year time series of Landsat imagery. Remote Sens Environ, 179: 196–209
https://doi.org/10.1016/j.rse.2016.03.031
23 H Liu, K C Jezek (2004). Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods. Int J Remote Sens, 25(5): 937–958
https://doi.org/10.1080/0143116031000139890
24 B Ma, L Wu, X Zhang, X Li, Y Liu, S Wang (2014). Locally adaptive unmixing method for lake-water area extraction based on MODIS 250 m bands. Int J Appl Earth Obs Geoinf, 33(1): 109–118
https://doi.org/10.1016/j.jag.2014.05.002
25 S K McFeeters (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens, 17(7): 1425–1432
https://doi.org/10.1080/01431169608948714
26 K C Mertens, B De Baets, L P C Verbeke, R R De Wulf (2006). A sub-pixel mapping algorithm based on sub-pixel/pixel spatial attraction models. Int J Remote Sens, 27(15): 3293–3310
https://doi.org/10.1080/01431160500497127
27 P S Mujabar, N Chandrasekar (2013). Shoreline change analysis along the coast between Kanyakumari and Tuticorin of India using remote sensing and GIS. Arab J Geosci, 6(3): 647–664
https://doi.org/10.1007/s12517-011-0394-4
28 N J Murray, R S Clemens, S R Phinn, H P Possingham, R A Fuller (2014). Tracking the rapid loss of tidal wetlands in the Yellow Sea. Front Ecol Environ, 12(5): 267–272
https://doi.org/10.1890/130260
29 F Nunziata, M Migliaccio, X Li, X Ding (2014). Coastline extraction using dual-polarimetric COSMO-SkyMed PingPong Mode SAR Data. IEEE Geosci Remote Sens Lett, 11(1): 104–108
https://doi.org/10.1109/LGRS.2013.2247561
30 M J Pajak, S Leatherman (2002). The high water line as shoreline indicator. J Coast Res, 18(2): 329–337
31 J F Pekel, A Cottam, N Gorelick, A S Belward (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418–422
https://doi.org/10.1038/nature20584
32 A F Rahman, D Dragoni, B El-Masri (2011). Response of the Sundarbans coastline to sea level rise and decreased sediment flow: a remote sensing assessment. Remote Sens Environ, 115(12): 3121–3128
https://doi.org/10.1016/j.rse.2011.06.019
33 J H Ryu, J S Won, K D Min (2002). Waterline extraction from Landsat TM data in a tidal flat: a case study in Gomso Bay, Korea. Remote Sens Environ, 83(3): 442–456
https://doi.org/10.1016/S0034-4257(02)00059-7
34 T Sankey, N Glenn (2011). Landsat-5 TM and Lidar fusion for sub-pixel juniper tree cover estimates in a western rangeland. Photogramm Eng Remote Sensing, 77(12): 1241–1248
https://doi.org/10.14358/PERS.77.12.1241
35 P Santos, A J Negri (1997). A comparison of the normalized difference vegetation index and rainfall for the Amazon and northeastern Brazil. J Appl Meteorol, 36(7): 958–965
https://doi.org/10.1175/1520-0450(1997)036<0958:ACOTND>2.0.CO;2
36 Y F Shi, X C Li (2010). Land use dynamic evolution simulation of the north branch of Yangtze River Estuary based on SLEUTH model. Modern Surveying & Mapping, 3: 003 (in Chinese)
37 W Z Su (2017). Measuring the past 20 years of urban-rural land growth in flood-prone areas in the developed Taihu Lake watershed, China. Front Earth Sci, 11(2): 361–371
38 E R Thieler, E A Himmelstoss, J L Zichichi, A Ergul (2009). The Digital Shoreline Analysis System (DSAS) version 4.0-An ArcGIS Extension for Calculating Shoreline Change. US Geological Survey
39 F Wang (1990). Fuzzy supervised classification of remote sensing images. IEEE Trans Geosci Remote Sens, 28(2): 194–201
https://doi.org/10.1109/36.46698
40 A F Wolf (2012). Using Worldview-2 Vis-NIR multispectral imagery to support land mapping and feature extraction using normalized difference index ratios SPIE Defense, Security, and Sensing. International Society for Optics and Photonics, Vol 8390
41 H Xie, X Luo, X Xu, H Pan, X Tong (2016 a). Evaluation of Landsat 8 OLI imagery for unsupervised inland water extraction. Int J Remote Sens, 37(8): 1826–1844
https://doi.org/10.1080/01431161.2016.1168948
42 H Xie, X Luo, X Xu, H Pan, X Tong (2016 b). Automated subpixel surface water mapping from heterogeneous urban environments using Landsat 8 OLI Imagery. Remote Sens, 8(7): 584
https://doi.org/10.3390/rs8070584
43 H Xie, X Luo, X Xu, X Tong, Y Jin, H Pan, X Zhou B (2014). New hyperspectral difference water index for the extraction of urban water bodies by the use of airborne hyperspectral images. J Appl Remote Sens, 8(1): 085098
https://doi.org/10.1117/1.JRS.8.085098
44 X Xu, Y Zhong, L Zhang (2014). A sub-pixel mapping method based on an attraction model for multiple shifted remotely sensed images. Neurocomputing, 134: 79–91
https://doi.org/10.1016/j.neucom.2012.12.078
45 C Yang, J H Everitt, J M Bradford (2008). Yield estimation from hyperspectral imagery using spectral angle mapper (SAM). Trans ASABE, 51(2): 729–737
https://doi.org/10.13031/2013.24370
46 Y Yang, Y Liu, M Zhou, S Zhang, W Zhan, C Sun, Y Duan (2015). Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach. Remote Sens Environ, 171: 14–32
https://doi.org/10.1016/j.rse.2015.10.005
47 Y Zha, J Gao, S Ni (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens, 24(3): 583–594
https://doi.org/10.1080/01431160304987
48 D Zhou, S Zhao, S Liu, L Zhang, C Zhu (2014). Surface urban heat island in China’s 32 major cities: spatial patterns and drivers. Remote Sens Environ, 152(152): 51–61
https://doi.org/10.1016/j.rse.2014.05.017
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