Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability -- Journal of Oceanology and Limnology,2019,37(6):1955-1970

	Cite this paper:
	YANG Fengshuo, YANG Xiaomei, WANG Zhihua, LU Chen, LI Zhi, LIU Yueming. Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability[J]. Journal of Oceanology and Limnology, 2019, 37(6): 1955-1970

Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability

YANG Fengshuo^1,2, YANG Xiaomei^1,2,4, WANG Zhihua¹, LU Chen^1,2, LI Zhi^2,3, LIU Yueming^1,2

1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
4 Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Abstract:

Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation. Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover, coastal areas often encounter significant cloud cover, especially in tropical areas, which makes the classification in those areas non-ideal. To solve this problem, we proposed a framework of combining medium-resolution optical images and synthetic aperture radar (SAR) data with the recently popular object-based image analysis (OBIA) method and used the Landsat Operational Land Imager (OLI) and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images acquired in Singapore in 2017 as a case study. We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size, and the other is the classification feature. Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80, and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features, especially in areas with cloud cover. Based on the land cover generated by this framework, we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km² over the past decade. To clarify the disaster response plan for different geographical environments, we classified risk based on altitude and distance from shore. The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk; these regions may need to focus on strengthening disaster prevention construction. This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters, especially those in cloudy coastal areas.

Received: 2018-09-26 Revised: 2019-01-06

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References:
Ban Y F, Hu H T, Rangel I M. 2010.Fusion of quickbird MS and RADARSAT SAR data for urban land-cover mapping:object-based and knowledge-based approach.International Journal of Remote Sensing, 31(6):1 391-1 410, https://doi.org/10.1080/01431160903475415.
Breiman L. 2001. Random forests. Machine Learning, 45(1):5-32, https://doi.org/10.1023/A:1010933404324.
Buono A, Nunziata F, Migliaccio M, Yang X, Li X. 2017.Classification of the Yellow River delta area using fully polarimetric SAR measurements. International Journal of Remote Sensing, 38(23):6 714-6 734, https://doi.org/10.1080/01431161.2017.1363437.
Chen W S, Gou S P, Wang X L, Li X F, Jiao L C. 2018.Classification of PolSAR images using multilayer autoencoders and a self-paced learning approach. Remote Sensing, 10(1):110, https://doi.org/10.3390/rs10010110.
Cho M A, Mathieu R, Asner G P, Naidoo L, Van Aardt J, Ramoelo A, Debba P, Wessels K, Main R, Smit I P J, Erasmus B. 2012. Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system. Remote Sensing of Environment, 125:214-226, https://doi.org/10.1016/j.rse.2012.07.010.
Congalton R G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1):35-46, https://doi.org/10.1016/0034-4257(91)90048-B.
Ding X W, Li X F. 2011. Monitoring of the water-area variations of Lake Dongting in China with ENVISAT ASAR images. International Journal of Applied Earth Observation and Geoinformation, 13(6):894-901, https://doi.org/10.1016/j.jag.2011.06.009.
Ding X W, Li X F. 2014. Shoreline movement monitoring based on SAR images in Shanghai, China. International Journal of Remote Sensing, 35(11-12):3 994-4 008, https://doi.org/10.1080/01431161.2014.916480.
Ding X W, Nunziata F, Li X F, Migliaccio M. 2015.Performance analysis and validation of waterline extraction approaches using single- and dual-polarimetric SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3):1 019-1 027, https://doi.org/10.1109/JSTARS.2014.2362511.
Dong J W, Xiao X M, Chen B Q, Torbick N, Jin C, Zhang G L, Biradar C. 2013. Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery. Remote Sensing of Environment, 134:392-402, https://doi.org/10.1016/j.rse.2013.03.014.
Dong J W, Xiao X M, Sheldon S, Biradar C, Duong N D, Hazarika M. 2012. A comparison of forest cover maps in Mainland Southeast Asia from multiple sources:PALSAR, MERIS, MODIS and FRA. Remote Sensing of Environment, 127:60-73, https://doi.org/10.1016/j.rse.2012.08.022.
Esch T, Marconcini M, Felbier A, Roth A, Heldens W, Huber M, Schwinger M, Taubenböck H, Müller A, Dech S. 2013.Urban footprint processor-fully automated processing chain generating settlement masks from global data of the TanDEM-X mission. IEEE Geoscience and Remote Sensing Letters, 10(6):1 617-1 621, https://doi.org/10.1109/LGRS.2013.2272953.
Gibril M B A, Bakar S A, Yao K, Idrees M O, Pradhan B. 2017.Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area. Geocarto International, 32(7):735-748, https://doi.org/10.1080/10106049.2016.1170893.
Gong S Q, Hagan D F T, Wu X Y, Wang G J. 2018b. Spatiotemporal analysis of precipitable water vapour over northwest china utilizing MERSI/FY-3A products.International Journal of Remote Sensing, 39(10):3 094-3 110, https://doi.org/10.1080/01431161.2018.1437298.
Gong S Q, Hagan D F, Lu J, Wang G J. 2018a. Validation on MERSI/FY-3A precipitable water vapor product.Advances in Space Research, 61(1):413-425, https://doi.org/10.1016/j.asr.2017.10.005.
Gong S Q. 2018. Evaluation of maritime aerosol optical depth and precipitable water vapor content from the Microtops II Sun photometer. Optik, 169:1-7, https://doi.org/10.1016/j.ijleo.2018.05.025.
Gou S P, Li X F, Yang X F. 2016. Coastal zone classification with fully polarimetric SAR imagery. IEEE Geoscience and Remote Sensing Letters, 13(11):1 616-1 620, https://doi.org/10.1109/LGRS.2016.2597965.
Greatbatch I. 2012. Polarimetric radar imaging:from basics to applications, by Jong-Sen Lee and Eric Pottier.International Journal of Remote Sensing, 33(2):661-662, https://doi.org/10.1080/01431161.2010.535193.
Hayes M M, Miller S N, Murphy M A. 2014. High-resolution landcover classification using Random Forest. Remote Sensing Letters, 5(2):112-121, https://doi.org/10.1080/2150704X.2014.882526.
Huang X D, Wang J F, Shang J L, Liao C H, Liu J G. 2017.Application of polarization signature to land cover scattering mechanism analysis and classification using multi-temporal C-band polarimetric RADARSAT-2 imagery. Remote Sensing of Environment, 193:11-28, https://doi.org/10.1016/j.rse.2017.02.014.
Huete A R, Liu H Q, Batchily K, Van Leeuwen W. 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59(3):440-451, https://doi.org/10.1016/S0034-4257(96)00112-5.
Isaac E, Easwarakumar K S, Isaac J. 2017. Urban landcover classification from multispectral image data using optimized AdaBoosted random forests. Remote Sensing Letters, 8(4):350-359, https://doi.org/10.1080/2150704X.2016.1274443.
Jiao X F, Zhang Y, Guindon B. 2015. Synergistic use of RADARSAT-2 ultra fine and fine quad-pol data to map oil sands infrastructure land:object-based approach.International Journal of Applied Earth Observation and Geoinformation, 38:193-203, https://doi.org/10.1016/j.jag.2015.01.007.
Kim M, Warner T A, Madden M, Atkinson D S. 2011. Multiscale GEOBIA with very high spatial resolution digital aerial imagery:scale, texture and image objects.International Journal of Remote Sensing, 32(10):2 825-2 850, https://doi.org/10.1080/01431161003745608.
Laliberte A S, Rango A. 2009. Texture and scale in objectbased analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery. IEEE Transactions on Geoscience and Remote Sensing, 47(3):761-770, https://doi.org/10.1109/TGRS.2008.2009355.
Lehmann E A, Caccetta P, Lowell K, Mitchell A, Zhou Z S, Held A, Milne T, Tapley I. 2015. SAR and optical remote sensing:assessment of complementarity and interoperability in the context of a large-scale operational forest monitoring system. Remote Sensing of Environment, 156:335-348, https://doi.org/10.1016/j.rse.2014.09.034.
Li M C, Ma L, Blaschke T, Cheng L, Tiede D. 2016. A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. International JournalofAppliedEarthObservationandGeoinformation, 49:87-98, https://doi.org/10.1016/j.jag.2016.01.011.
Liu Q R, Ruan C Q, Zhong S, Li J, Yin Z H, Lian X H. 2018.Risk assessment of storm surge disaster based on numerical models and remote sensing. International JournalofAppliedEarthObservationandGeoinformation, 68:20-30, https://doi.org/10.1016/j.jag.2018.01.016.
Ma L, Cheng L, Li M C, Liu Y X, Ma X X. 2015. Training set size, scale, and features in geographic object-based image analysis of very high resolution unmanned aerial vehicle imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 102:14-27, https://doi.org/10.1016/j.isprsjprs.2014.12.026.
Malahlela O, Cho M A, Mutanga O. 2014. Mapping canopy gaps in an indigenous subtropical coastal forest using high-resolution WorldView-2 data. International Journal of Remote Sensing, 35(17):6 397-6 417, https://doi.org/10.1080/01431161.2014.954061.
Nascimento Jr W R, Souza-Filho P W M, Proisy C, Lucas R M, Rosenqvist A. 2013. Mapping changes in the largest continuous amazonian mangrove belt using object-based classification of multisensor satellite imagery. Estuarine, Coastal and Shelf Science, 117:83-93, https://doi.org/10.1016/j.ecss.2012.10.005.
Nunziata F, Migliaccio M, Li X F, Ding X W. 2014. Coastline extraction using dual-polarimetric COSMO-SkyMed PingPong mode SAR data. IEEE Geoscience and Remote Sensing Letters, 11(1):104-108, https://doi.org/10.1109/LGRS.2013.2247561.
Pesaresi M, Guo H D, Blaes X, Ehrlich D, Ferri S, Gueguen L, Halkia M, Kauffmann M, Kemper T, Lu L L, MarinHerrera M A, Ouzounis G K, Scavazzon M, Soille P, Syrris V, Zanchetta L. 2013. A global human settlement layer from optical HR/VHR RS data:concept and first results. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(5):2 102-2 131, https://doi.org/10.1109/JSTARS.2013.2271445.
Peters J, Van Coillie F, Westra T, De Wulf R. 2011. Synergy of very high resolution optical and radar data for objectbased olive grove mapping. International Journal of Geographical Information Science, 25(6):971-989, https://doi.org/10.1080/13658816.2010.515946.
Shimada M, Isoguchi O, Tadono T, Isono K. 2009. PALSAR radiometric and geometric calibration. IEEE Transactions on Geoscience and Remote Sensing, 47(12):3 915-3 932, https://doi.org/10.1109/TGRS.2009.2023909.
Sidhu N, Pebesma E, Wang Y C. 2017. Usability study to assess the IGBP land cover classification for Singapore.Remote Sensing, 9(10):1 075, https://doi.org/10.3390/rs9101075.
State Oceanic Administration People's Republic of China. 2015. Guideline for risk assessment and zoning of storm surge disaster. State Oceanic Administration People's Republic of China. http://www.gzagri.gov.cn/gzsnyj/bzgf/201807/69dc480f97824378a8183203810038c8/files/0e7788e1cffd4ca69668fea125b0a3a1.pdf. Accessed on 2018-05-25.
Wang B, Ono A, Muramatsu K, Fujiwara N. 1999. Automated detection and removal of clouds and their shadows from Landsat TM images. IEICE Transactions on Information and Systems, E82-D(2):453-460.
Wang L, Sousa W P, Gong P. 2004. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International Journal of Remote Sensing, 25(24):5 655-5 668, https://doi.org/10.1080/014311602331291215.
Wang W S, Yang X F, Li X F, Chen K S, Liu G H, Li Z W, Gade M. 2017. A fully polarimetric SAR imagery classification scheme for mud and sand flats in intertidal zones. IEEE Transactions on Geoscience and Remote Sensing, 55(3):1 734-1 742, https://doi.org/10.1109/TGRS.2016.2631632.
Wang Z H, Lu C, Yang X M. 2018a. Exponentially sampling scale parameters for the efficient segmentation of remotesensing images. International Journal of Remote Sensing, 39(6):1 628-1 654, https://doi.org/10.1080/01431161.2017.1410297.
Wang Z H, Meng F, Yang X M, Yang F S, Fang Y. 2016. Study on the automatic selection of segmentation scale parameters for high spatial resolution remote sensing images. Journal of Geo-information Science, 18(5):639-648, https://doi.org/10.3724/SP.J.1047.2016.00639. (in Chinese with English abstract)
Wang Z H, Yang X M, Lu C, Yang F S. 2018b. A scale selfadapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images.International Journal of Applied Earth Observation and Geoinformation, 69:88-98, https://doi.org/10.1016/j.jag.2018.03.001.
Watmough G R, Atkinson P M, Hutton C W. 2011. A combined spectral and object-based approach to transparent cloud removal in an operational setting for Landsat ETM+.International Journal of Applied Earth Observation and Geoinformation,13(2):220-227, https://doi.org/10.1016/j.jag.2010.11.006.
Witharana C, Civco D L. 2014. Optimizing multi-resolution segmentation scale using empirical methods:exploring the sensitivity of the supervised discrepancy measure euclidean distance 2 (ED2). ISPRS Journal of Photogrammetry and Remote Sensing, 87:108-121, https://doi.org/10.1016/j.isprsjprs.2013.11.006.
Xu H Q. 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14):3 025-3 033, https://doi.org/10.1080/01431160600589179.
Xu R, Zhang H S, Lin H. 2017. Urban impervious surfaces estimation from optical and SAR imagery:a comprehensive comparison. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9):4 010-4 021, https://doi.org/10.1109/JSTARS.2017.2706747.
Yue A Z, Yang J Y, Zhang C, Su W, Yun W J, Zhu D H, Liu S X, Wang Z W. 2012. The optimal segmentation scale identification using multispectral WorldView-2 images.Sensor Letters, 10(1-2):285-291, https://doi.org/10.1166/sl.2012.1860.
Zha Y, Gao J, Ni S. 2003. Use of normalized difference builtup index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3):583-594.
Zhang X L, Xiao P F, Song X Q, She J F. 2013. Boundaryconstrained multi-scale segmentation method for remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 78:15-25, https://doi.org/10.1016/j.isprsjprs.2013.01.002.
Zhu Z, Woodcock C E, Rogan J, Kellndorfer J. 2012.Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data. Remote Sensing of Environment, 117:72-82, https://doi.org/10.1016/j.rse.2011.07.020.
Zhu Z, Woodcock C E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118:83-94, https://doi.org/10.1016/j.rse.2011.10.028.