Advertisement

Frontiers of Earth Science

, Volume 13, Issue 3, pp 478–494 | Cite as

Automatic sub-pixel coastline extraction based on spectral mixture analysis using EO-1 Hyperion data

  • Zhonghua HongEmail author
  • Xuesu Li
  • Yanling HanEmail author
  • Yun Zhang
  • Jing Wang
  • Ruyan Zhou
  • Kening Hu
Research Article

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 subpixel 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 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

The work described in this paper was substantially supported by the National Natural Science Foundation of China (Grant Nos. 41401489 and 41376178), Shanghai Foundation for University Youth Scholars (Project No. ZZHY13033), and the Innovation Programme of the Shanghai Municipal Education Commission (Project No. 15ZZ082).

References

  1. Alonzo M, Bookhagen B, Roberts D A (2014). Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sens Environ, 148(148): 70–83Google Scholar
  2. Atkinson P M (1997). Mapping Sub-Pixel Boundaries from Remotely Sensed Images. In: Kemp Z, ed. Innovations in GIS 4. London: Taylor and Francis: 166–180Google Scholar
  3. Barry P (2001). EO-1 Hyperion Science Data User’s Guide, Level 1_B. TRW Space. Defense and Information Systems, 555–557Google Scholar
  4. Bird E C F (1985). Coastline Changes. A global review. New York: John Wiley and Sons Inc., 108Google Scholar
  5. Boak E H, Turner I L (2005). Shoreline definition and detection: a review. J Coast Res, 21(4): 688–703Google Scholar
  6. Bouchahma M, Yan W (2014). Monitoring shoreline change on Djerba Island using GIS and multi-temporal satellite data. Arab J Geosci, 7 (9): 3705–3713Google Scholar
  7. Delhez E J M, Barth A (2011). Science based management of coastal waters. J Mar Syst, 88(1): 1–2Google Scholar
  8. Di K, Wang J, Ma R, Li R (2003). Automatic shoreline extraction from high resolution IKONOS satellite imagery. Cortex, 49(1): 184–199Google Scholar
  9. Feng Y, Liu Y, Liu D (2015). Shoreline mapping with cellular automata and the shoreline progradation analysis in Shanghai, China from 1979 to 2008. Arab J Geosci, 8(7): 4337–4351Google Scholar
  10. Feyisa G L, Meilby H, Fensholt R, Proud S R (2014). Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sens Environ, 140(1): 23–35Google Scholar
  11. Foody G M (1996). Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data. Int J Remote Sens, 17(7): 1317–1340Google Scholar
  12. Foody G M, Muslim A M, Atkinson P M (2003). Super-resolution Mapping of the Shoreline through Soft Classification Analyses. IEEE International Geoscience and Remote Sensing Symposium, (6): 3429–3431Google Scholar
  13. Franke J, Roberts D A, Halligan K, Menz G (2009). Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sens Environ, 113(8): 1712–1723Google Scholar
  14. Frazier P S, Page K J (2000). Water body detection and delineation with Landsat TMdata. Photogramm Eng Remote Sensing, 66(12): 1461–1468Google Scholar
  15. Gens R (2010). Remote sensing of coastlines: detection, extraction and monitoring. Int J Remote Sens, 31(7): 1819–1836Google Scholar
  16. Güneralp I, Filippi A M, Hales B U (2013). River-flow boundary delineation from digital aerial photography and ancillary images using Support Vector Machines. GIsci Remote Sens, 50(1): 1–25Google Scholar
  17. Harris A T (2006). Spectral mapping tools from the earth sciences applied to spectral microscopy data. Cytometry A, 69A(8): 872–879Google Scholar
  18. Keshava N, Mustard J F (2002). Spectral unmixing. IEEE Signal Process Mag, 19(1): 44–57Google Scholar
  19. Lee J S, Jurkevich I (1990). Coastline detection and tracing In SAR images. IEEE Trans Geosci Remote Sens, 28(4): 662–668Google Scholar
  20. Li R, Di K, Ma R (2003). 3-D shoreline extraction from IKONOS satellite imagery. Mar Geod, 26(1–2): 107–115Google Scholar
  21. Li R, Keong C W, Ramcharan E, Kjerfve E, Willis D (1998). A coastal GIS for shoreline monitoring and management-case study in Malaysia. Surveying and Land Information Systems, 58(3): 157–166Google Scholar
  22. Li W, Gong P (2016). Continuous monitoring of coastline dynamics in western Florida with a 30-year time series of Landsat imagery. Remote Sens Environ, 179: 196–209Google Scholar
  23. Liu H, Jezek K C (2004). Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods. Int J Remote Sens, 25(5): 937–958Google Scholar
  24. Ma B, Wu L, Zhang X, Li X, Liu Y, Wang S (2014). Locally adaptive unmixing method for lake-water area extraction based on MODIS 250 m bands. Int J Appl Earth Obs Geoinf, 33(1): 109–118Google Scholar
  25. McFeeters S K (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens, 17(7): 1425–1432Google Scholar
  26. Mertens K C, De Baets B, Verbeke L P C, De Wulf R R (2006). A subpixel mapping algorithm based on sub-pixel/pixel spatial attraction models Int J Remote Sens, 27(15): 3293–3310Google Scholar
  27. Mujabar P S, Chandrasekar N (2013). Shoreline change analysis along the coast between Kanyakumari and Tuticorin of India using remote sensing and GIS. Arab J Geosci, 6(3): 647–664Google Scholar
  28. Murray N J, Clemens R S, Phinn S R, Possingham H P, Fuller R A (2014). Tracking the rapid loss of tidal wetlands in the Yellow Sea. Front Ecol Environ, 12(5): 267–272Google Scholar
  29. Nunziata F, Migliaccio M, Li X, Ding X (2014). Coastline extraction using dual-polarimetric COSMO-SkyMed PingPong Mode SAR Data. IEEE Geosci Remote Sens Lett, 11(1): 104–108Google Scholar
  30. Pajak M J, Leatherman S (2002). The high water line as shoreline indicator. J Coast Res, 18(2): 329–337Google Scholar
  31. Pekel J F, Cottam A, Gorelick N, Belward A S (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418–422Google Scholar
  32. Rahman A F, Dragoni D, El-Masri B (2011). Response of the Sundarbans coastline to sea level rise and decreased sediment flow: a remote sensing assessment. Remote Sens Environ, 115(12): 3121–3128Google Scholar
  33. Ryu J H, Won J S, Min K D (2002). Waterline extraction from Landsat TMdata in a tidal flat: a case study in Gomso Bay, Korea. Remote Sens Environ, 83(3): 442–456Google Scholar
  34. Sankey T, Glenn N (2011). Landsat-5 TM and Lidar fusion for sub-pixel juniper tree cover estimates in a western rangeland. Photogramm Eng Remote Sensing, 77(12): 1241–1248Google Scholar
  35. Santos P, Negri A J (1997). A comparison of the normalized difference vegetation index and rainfall for the Amazon and northeastern Brazil. J Appl Meteorol, 36(7): 958–965Google Scholar
  36. Shi Y F, Li X C (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)Google Scholar
  37. Su W Z (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): 361–371Google Scholar
  38. Thieler E R, Himmelstoss E A, Zichichi J L, Ergul A (2009). The Digital Shoreline Analysis System (DSAS) version 4.0-An ArcGIS Extension for Calculating Shoreline Change. US Geological SurveyGoogle Scholar
  39. Wang F (1990). Fuzzy supervised classification of remote sensing images. IEEE Trans Geosci Remote Sens, 28(2): 194–201Google Scholar
  40. Wolf A F (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 8390Google Scholar
  41. Xie H, Luo X, Xu X, Pan H, Tong X (2016 a). Evaluation of Landsat 8 OLI imagery for unsupervised inland water extraction. Int J Remote Sens, 37(8): 1826–1844Google Scholar
  42. Xie H, Luo X, Xu X, Pan H, Tong X (2016 b). Automated subpixel surface water mapping from heterogeneous urban environments using Landsat 8 OLI Imagery. Remote Sens, 8(7): 584Google Scholar
  43. Xie H, Luo X, Xu X, Tong X, Jin Y, Pan H, Zhou B X (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): 085098Google Scholar
  44. Xu X, Zhong Y, Zhang L (2014). A sub-pixel mapping method based on an attraction model for multiple shifted remotely sensed images. Neurocomputing, 134: 79–91Google Scholar
  45. Yang C, Everitt J H, Bradford J M (2008). Yield estimation from hyperspectral imagery using spectral angle mapper (SAM). Trans ASABE, 51(2): 729–737Google Scholar
  46. Yang Y, Liu Y, Zhou M, Zhang S, Zhan W, Sun C, Duan Y (2015). Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach. Remote Sens Environ, 171: 14–32Google Scholar
  47. Zha Y, Gao J, Ni S (2003). Use of normalized difference built-up index in automatically mapping urban areas from TMimagery. Int J Remote Sens, 24(3): 583–594Google Scholar
  48. Zhou D, Zhao S, Liu S, Zhang L, Zhu C (2014). Surface urban heat island in China’s 32 major cities: spatial patterns and drivers. Remote Sens Environ, 152(152): 51–61Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information TechnologyShanghai Ocean UniversityShanghaiChina
  2. 2.Key Laboratory of Fisheries InformationMinistry of AgricultureShanghaiChina

Personalised recommendations