复杂环境下高分二号遥感影像的城市地表水体提取
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摘要
水体指数可以抑制背景噪声和提高地表水体的可分性,已经广泛用于地表水体提取。传统FCM聚类算法考虑了地物的不确定性,但没有顾及地物的邻域空间信息,对背景异质性比较敏感。针对传统FCM聚类算法的不足,提出一种可变邻域的区域FCM聚类算法。由于复杂环境下高分二号(GF-2)遥感影像的城市地表水体具有复杂异质背景和不确定性的特点,本文利用水体指数和区域FCM聚类算法的优点,提出一种整合水体指数和区域FCM的城市地表水体自动提取算法,该算法主要步骤包括:(1)去除影像阴影后计算归一化差分水体指数NDWI(Normalized Difference Water Index);(2)区域FCM聚类算法;(3)整合水体指数和区域FCM聚类的城市地表水体自动提取算法。最后采用两景GF-2高分辨率遥感影像(广州和武汉)进行实验,验证了该算法的有效性,并与经典地表水体提取算法进行对比分析。实验结果表明:该算法具有较高的水体提取精度,城市地表水体边界既具有较好的区域完整性又保持了局部细节,同时对城市地表水体复杂背景噪声具有较好的抑制作用,有效减少传统FCM聚类算法的"胡椒盐"现象。
The water index can suppress background noise and increase the separability of surface water. Thus, it has been widely used for surface water extraction. Traditional FCM clustering algorithm considers the uncertainty of ground objects without neighborhood spatial information, which is sensitive to background heterogeneity. On the basis of the shortcomings of traditional FCM clustering algorithms, this study proposed a regional FCM clustering algorithm and applied it to extract city surface water in complex environment regions using GF-2 remote sensing imagery. The main steps of the method include(1)Calculating the normalized difference water index after the removal of shadows;(2) Presenting a regional FCM clustering algorithm;(3)Proposing the urban surface water automatic extraction algorithm by combining the water body index and the regional FCM clustering algorithm. Finally, the proposed method was carried out on two GF-2 high-resolution remote sensing image data located in Guangzhou and Wuhan. The experimental results showed that the proposed method has better accuracy and water boundary than state-of-the-art methods. The proposed method also retains regional integrity and local details of surface water objects while effectively inhibiting noise from urban surface water in the complex background, thereby reducing the "salt and pepper"phenomenon found in traditional FCM clustering algorithm.
The water index can suppress background noise and increase the separability of surface water. Thus, it has been widely used for surface water extraction. Traditional FCM clustering algorithm considers the uncertainty of ground objects without neighborhood spatial information, which is sensitive to background heterogeneity. On the basis of the shortcomings of traditional FCM clustering algorithms, this study proposed a regional FCM clustering algorithm and applied it to extract city surface water in complex environment regions using GF-2 remote sensing imagery. The main steps of the method include(1)Calculating the normalized difference water index after the removal of shadows;(2) Presenting a regional FCM clustering algorithm;(3)Proposing the urban surface water automatic extraction algorithm by combining the water body index and the regional FCM clustering algorithm. Finally, the proposed method was carried out on two GF-2 high-resolution remote sensing image data located in Guangzhou and Wuhan. The experimental results showed that the proposed method has better accuracy and water boundary than state-of-the-art methods. The proposed method also retains regional integrity and local details of surface water objects while effectively inhibiting noise from urban surface water in the complex background, thereby reducing the "salt and pepper"phenomenon found in traditional FCM clustering algorithm.
引文
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Chen C,Qin Q M,Zhang N,Li J,Chen L,Wang J,Qin X B and Yang X C.2014.Extraction of bridges over water from high-resolution optical remote-sensing images based on mathematical morphology.International Journal of Remote Sensing,35(10):3664-3682[DOI:10.1080/01431161.2014.915437]
Chen N C,Liu D D and Du W Y.2017.Improved balloon snake method for water boundary extraction in remote sensing images.Journal of Remote sensing,21(3):425-433(陈能成,刘丹丹,杜文英.2017.改进Balloon Snake算法提取遥感影像水体边界.遥感学报,21(3):425-433)[DOI:10.11834/jrs.20176126]
Davranche A,Lefebvre G and Poulin B.2010.Wetland monitoring using classification trees and SPOT-5 seasonal time series.Remote Sensing of Environment,114(3):552-562[DOI:10.1016/j.rse.2009.10.009]
Du Y,Zhang Y H,Ling F,Wang Q M,Li W B and Li X D.2016.Water bodies'mapping from sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band.Remote Sensing,8(4):354[DOI:10.3390/rs8040354]
Du Y Y and Zhou C F.1998.Automatically extracting remote sensing information for water bodies.Journal of Remote Sensing,2(4):264-269(杜云艳,周成虎.1998.水体的遥感信息自动提取方法.遥感学报,2(4):264-269)[DOI:10.11834/jrs.19980406]
Feyisa G L,Meilby H,Fensholt R and Proud S R.2014.Automated water extraction index:a new technique for surface water mapping using Landsat imagery.Remote Sensing of Environment,140:23-35[DOI:10.1016/j.rse.2013.08.029]
Ghosh A,Mishra N S and Ghosh S.2011.Fuzzy clustering algorithms for unsupervised change detection in remote sensing images.Information Sciences,181(4):699-715[DOI:10.1016/j.ins.2010.10.016]
Giustarini L,Hostache R,Matgen P,Schumann J P,Bates P D and Mason D C.2013.A change detection approach to flood mapping in urban areas using terraSAR-x.IEEE Transactions on Geoscience and Remote Sensing,51(4):2417-2430[DOI:10.1109/TGRS.2012.2210901]
Huang X,Xie C,Fang X and Zhang L P.2015.Combining pixel-and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery.IEEEJournal of Selected Topics in Applied Earth Observations and Remote Sensing,8(5):2097-2110[DOI:10.1109/JSTARS.2015.2420713]
Jawak S D and Luis A J.2015.A rapid extraction of water body features from Antarctic coastal oasis using very high-resolution satellite remote sensing data.Aquatic Procedia,4:125-132[DOI:10.1016/j.aqpro.2015.02.018]
Kang J Y.2009.Novel modified fuzzy c-means clustering algorithm considering pixel spatial information.Chinese Journal of Scientific Instrument,30(1):208-212(康家银.2009.一种改进的顾及像素空间信息的FCM聚类算法.仪器仪表学报,30(1):208-212)[DOI:10.19650/j.cnki.cjsi.2009.01.039]
Klein I,Dietz A J,Gessner U,Galayeva A,Myrzakhmetov A and Kuenzer C.2014.Evaluation of seasonal water body extents in Central Asia over the past 27 years derived from medium-resolution remote sensing data.International Journal of Applied Earth Observation and Geoinformation,26:335-349[DOI:10.1016/j.jag.2013.08.004]
Klein I,Gessner U,Dietz A J and Kuenzer C.2017.Global WaterPack-A 250 m resolution dataset revealing the daily dynamics of global inland water bodies.Remote Sensing of Environment,198:345-362[DOI:10.1016/j.rse.2017.06.045]
Liu X F and He B B.2011.Remote sensing image classification based on neighbor sample density and membership weighted FCM algorithm.Chinese Journal of Scientific Instrument,32(10):2242-2247(刘小芳,何彬彬.2011.近邻样本密度和隶属度加权FCM算法的遥感图像分类方法.仪器仪表学报,32(10):2242-2247)[DOI:10.19650/j.cnki.cjsi.2011.10.013]
Lu S L,Jia L,Zhang L,Wei Y P,Baig M H A,Zhai Z K,Meng J H,Li X S and Zhang G F.2017.Lake water surface mapping in the Tibetan plateau using the MODIS MOD09Q1 product.Remote Sensing Letters,8(3):224-233[DOI:10.1080/2150704X.2016.1260178]
McFeeters S K.1996.The use of the normalized difference water index(NDWI)in the delineation of open water features.International Journal of Remote Sensing,17(7):1425-1432[DOI:10.1080/01431169608948714]
Nath R K and Deb S K.2010.Water-body area extraction from high resolution satellite images-an introduction,review,and comparison.International Journal of Image Processing,3(6):353-372
Sharma R C,Tateishi R,Hara K and Nguyen L V.2015.Developing superfine water index(SWI)for global water cover mapping using MODIS data.Remote Sensing,7(10):13807-13841[DOI:10.3390/rs71013807]
Sun F D,Sun W X,Chen J and Gong P.2012.Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery.International Journal of Remote Sensing,33(21):6854-6875[DOI:10.1080/01431161.2012.692829]
Tian H F,Li W,Wu M Q,Huang N,Li G D,Li X and Niu Z.2017.Dynamic monitoring of the largest freshwater lake in china using a new water index derived from high spatiotemporal resolution sentinel-1a data.Remote Sensing,9(6):521[DOI:10.3390/rs9060521]
Tulbure M G and Broich M.2013.Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011.ISPRS Journal of Photogrammetry and Remote Sensing,79:44-52[DOI:10.1016/j.isprsjprs.2013.01.010]
Varis O and Vakkilainen P.2001.China's 8 challenges to water resources management in the first quarter of the 21st Century.Geomorphology,41(2/3):93-104[DOI:10.1016/S0169-555X(01)00107-6]
Wang S D,Baig M H A,Zhang L F,Jiang H L,Ji Y H,Zhao H Q and Tian J G.2015.A simple enhanced water index(EWI)for percent surface water estimation using Landsat data.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,8(1):90-97[DOI:10.1109/JSTARS.2014.2387196]
Wang S Y,Jiao H Z and Zhong Y F.2016.A modified FCM classifier constrained by conditional random field model for remote sensing imagery.Acta Geodaetica et Cartographica Sinica,45(12):1441-1447(王少宇,焦洪赞,钟燕飞.2016.条件随机场模型约束下的遥感影像模糊C-均值聚类算法.测绘学报,45(12):1441-1447)[DOI:10.11947/j.AGCS.2016.20150621]
Wang W H,Huang L and Xia L G.2015.Monitoring water distribution changes based on the supplemented multi-temporal HJ-1 data.Journal of Geo-information Science,17(9):1110-1118(王卫红,黄琳,夏列钢.2015.多时相HJ-1数据补充的水体分布时序变化监测.地球信息科学学报,17(9):1110-1118)
Xiao M S,Xiao Z,Wen Z C and Zhou L Q.2017.Improved FCM clustering algorithm based on spatial correlation and membership smoothing.Journal of Electronics and Information Technology,39(5):1123-1129(肖满生,肖哲,文志诚,周立前.2017.一种空间相关性与隶属度平滑的FCM改进算法.电子与信息学报,39(5):1123-1129)[DOI:10.11999/jeit160710]
Xie C,Huang X,Zeng W X and Fang X.2016a.A novel water index for urban high-resolution eight-band worldview-2 imagery.International Journal of Digital Earth,9(10):925-941[DOI:10.1080/17538947.2016.1170215]
Xie H,Luo X,Xu X,Pan H Y and Tong X H.2016b.Automated subpixel surface water mapping from heterogeneous urban environments using Landsat 8 OLI imagery.Remote Sensing,8(7):584[DOI:10.3390/rs8070584]
Xie L,Zhang H,Wang C and Chen F L.2016c.Water-body types identification in urban areas from radarsat-2 fully polarimetric SARdata.International Journal of Applied Earth Observation and Geoinformation,50:10-25[DOI:10.1016/j.jag.2016.02.012]
Xu H Q.2006.Modification of normalised difference water index(ND-WI)to enhance open water features in remotely sensed imagery.International Journal of Remote Sensing,27(14):3025-3033[DOI:10.1080/01431160600589179]
Yang X C,Zhao S S,Qin X B,Zhao N and Liang L G.2017.Mapping of urban surface water bodies from sentinel-2 MSI imagery at 10m resolution via NDWI-based image sharpening.Remote Sensing,9(6):596[DOI:10.3390/rs9060596]
Yang Y H,Liu Y X,Zhou M X,Zhang S Y,Zhan W F,Sun C and Duan Y W.2015.Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach.Remote Sensing of Environment,171:14-32[DOI:10.1016/j.rse.2015.10.005]
Yao F F,Wang C,Dong D,Luo J C,Shen Z F and Yang K H.2015.High-resolution mapping of urban surface water using ZY-3 multispectral imagery.Remote Sensing,7(9):12336-12355[DOI:10.3390/rs70912336]
Zeng C Q,Bird S,Luce J L and Wang J F.2015.A natural-rule-basedconnection(NRBC)method for river network extraction from high-resolution imagery.Remote Sensing,7(10):14055-14078[DOI:10.3390/rs71014055]
Zha L,Gong H L,Hu Z W and Du H Y.2015.The comparative research of water extraction based on high resolution imagery.Journal of Capital Normal University(Natural Science Edition),36(4):85-89(查力,宫辉力,胡卓玮,杜红悦.2015.高分影像水体信息提取对比研究.首都师范大学学报(自然科学版),36(4):85-89)[DOI:10.3969/j.issn.1004-9398.2015.04.019]
Zhang L P,Huang X,Huang B and Li P X.2006.A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery.IEEE Transactions on Geoscience and Remote Sensing,44(10):2950-2961[DOI:10.1109/TGRS.2006.876704]
Zhou Y N,Luo J C,Shen Z F,Hu X D and Yang H P.2014.Multiscale water body extraction in urban environments from satellite images.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,7(10):4301-4312[DOI:10.1109/JSTARS.2014.2360436]
Canaz S,Karsli F,Guneroglu A and Dihkan M.2015.Automatic boundary extraction of inland water bodies using LiDAR data.Ocean and Coastal Management,118:158-166[DOI:10.1016/j.ocecoaman.2015.07.024]
Chen C,Qin Q M,Zhang N,Li J,Chen L,Wang J,Qin X B and Yang X C.2014.Extraction of bridges over water from high-resolution optical remote-sensing images based on mathematical morphology.International Journal of Remote Sensing,35(10):3664-3682[DOI:10.1080/01431161.2014.915437]
Chen N C,Liu D D and Du W Y.2017.Improved balloon snake method for water boundary extraction in remote sensing images.Journal of Remote sensing,21(3):425-433(陈能成,刘丹丹,杜文英.2017.改进Balloon Snake算法提取遥感影像水体边界.遥感学报,21(3):425-433)[DOI:10.11834/jrs.20176126]
Davranche A,Lefebvre G and Poulin B.2010.Wetland monitoring using classification trees and SPOT-5 seasonal time series.Remote Sensing of Environment,114(3):552-562[DOI:10.1016/j.rse.2009.10.009]
Du Y,Zhang Y H,Ling F,Wang Q M,Li W B and Li X D.2016.Water bodies'mapping from sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band.Remote Sensing,8(4):354[DOI:10.3390/rs8040354]
Du Y Y and Zhou C F.1998.Automatically extracting remote sensing information for water bodies.Journal of Remote Sensing,2(4):264-269(杜云艳,周成虎.1998.水体的遥感信息自动提取方法.遥感学报,2(4):264-269)[DOI:10.11834/jrs.19980406]
Feyisa G L,Meilby H,Fensholt R and Proud S R.2014.Automated water extraction index:a new technique for surface water mapping using Landsat imagery.Remote Sensing of Environment,140:23-35[DOI:10.1016/j.rse.2013.08.029]
Ghosh A,Mishra N S and Ghosh S.2011.Fuzzy clustering algorithms for unsupervised change detection in remote sensing images.Information Sciences,181(4):699-715[DOI:10.1016/j.ins.2010.10.016]
Giustarini L,Hostache R,Matgen P,Schumann J P,Bates P D and Mason D C.2013.A change detection approach to flood mapping in urban areas using terraSAR-x.IEEE Transactions on Geoscience and Remote Sensing,51(4):2417-2430[DOI:10.1109/TGRS.2012.2210901]
Huang X,Xie C,Fang X and Zhang L P.2015.Combining pixel-and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery.IEEEJournal of Selected Topics in Applied Earth Observations and Remote Sensing,8(5):2097-2110[DOI:10.1109/JSTARS.2015.2420713]
Jawak S D and Luis A J.2015.A rapid extraction of water body features from Antarctic coastal oasis using very high-resolution satellite remote sensing data.Aquatic Procedia,4:125-132[DOI:10.1016/j.aqpro.2015.02.018]
Kang J Y.2009.Novel modified fuzzy c-means clustering algorithm considering pixel spatial information.Chinese Journal of Scientific Instrument,30(1):208-212(康家银.2009.一种改进的顾及像素空间信息的FCM聚类算法.仪器仪表学报,30(1):208-212)[DOI:10.19650/j.cnki.cjsi.2009.01.039]
Klein I,Dietz A J,Gessner U,Galayeva A,Myrzakhmetov A and Kuenzer C.2014.Evaluation of seasonal water body extents in Central Asia over the past 27 years derived from medium-resolution remote sensing data.International Journal of Applied Earth Observation and Geoinformation,26:335-349[DOI:10.1016/j.jag.2013.08.004]
Klein I,Gessner U,Dietz A J and Kuenzer C.2017.Global WaterPack-A 250 m resolution dataset revealing the daily dynamics of global inland water bodies.Remote Sensing of Environment,198:345-362[DOI:10.1016/j.rse.2017.06.045]
Liu X F and He B B.2011.Remote sensing image classification based on neighbor sample density and membership weighted FCM algorithm.Chinese Journal of Scientific Instrument,32(10):2242-2247(刘小芳,何彬彬.2011.近邻样本密度和隶属度加权FCM算法的遥感图像分类方法.仪器仪表学报,32(10):2242-2247)[DOI:10.19650/j.cnki.cjsi.2011.10.013]
Lu S L,Jia L,Zhang L,Wei Y P,Baig M H A,Zhai Z K,Meng J H,Li X S and Zhang G F.2017.Lake water surface mapping in the Tibetan plateau using the MODIS MOD09Q1 product.Remote Sensing Letters,8(3):224-233[DOI:10.1080/2150704X.2016.1260178]
McFeeters S K.1996.The use of the normalized difference water index(NDWI)in the delineation of open water features.International Journal of Remote Sensing,17(7):1425-1432[DOI:10.1080/01431169608948714]
Nath R K and Deb S K.2010.Water-body area extraction from high resolution satellite images-an introduction,review,and comparison.International Journal of Image Processing,3(6):353-372
Sharma R C,Tateishi R,Hara K and Nguyen L V.2015.Developing superfine water index(SWI)for global water cover mapping using MODIS data.Remote Sensing,7(10):13807-13841[DOI:10.3390/rs71013807]
Sun F D,Sun W X,Chen J and Gong P.2012.Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery.International Journal of Remote Sensing,33(21):6854-6875[DOI:10.1080/01431161.2012.692829]
Tian H F,Li W,Wu M Q,Huang N,Li G D,Li X and Niu Z.2017.Dynamic monitoring of the largest freshwater lake in china using a new water index derived from high spatiotemporal resolution sentinel-1a data.Remote Sensing,9(6):521[DOI:10.3390/rs9060521]
Tulbure M G and Broich M.2013.Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011.ISPRS Journal of Photogrammetry and Remote Sensing,79:44-52[DOI:10.1016/j.isprsjprs.2013.01.010]
Varis O and Vakkilainen P.2001.China's 8 challenges to water resources management in the first quarter of the 21st Century.Geomorphology,41(2/3):93-104[DOI:10.1016/S0169-555X(01)00107-6]
Wang S D,Baig M H A,Zhang L F,Jiang H L,Ji Y H,Zhao H Q and Tian J G.2015.A simple enhanced water index(EWI)for percent surface water estimation using Landsat data.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,8(1):90-97[DOI:10.1109/JSTARS.2014.2387196]
Wang S Y,Jiao H Z and Zhong Y F.2016.A modified FCM classifier constrained by conditional random field model for remote sensing imagery.Acta Geodaetica et Cartographica Sinica,45(12):1441-1447(王少宇,焦洪赞,钟燕飞.2016.条件随机场模型约束下的遥感影像模糊C-均值聚类算法.测绘学报,45(12):1441-1447)[DOI:10.11947/j.AGCS.2016.20150621]
Wang W H,Huang L and Xia L G.2015.Monitoring water distribution changes based on the supplemented multi-temporal HJ-1 data.Journal of Geo-information Science,17(9):1110-1118(王卫红,黄琳,夏列钢.2015.多时相HJ-1数据补充的水体分布时序变化监测.地球信息科学学报,17(9):1110-1118)
Xiao M S,Xiao Z,Wen Z C and Zhou L Q.2017.Improved FCM clustering algorithm based on spatial correlation and membership smoothing.Journal of Electronics and Information Technology,39(5):1123-1129(肖满生,肖哲,文志诚,周立前.2017.一种空间相关性与隶属度平滑的FCM改进算法.电子与信息学报,39(5):1123-1129)[DOI:10.11999/jeit160710]
Xie C,Huang X,Zeng W X and Fang X.2016a.A novel water index for urban high-resolution eight-band worldview-2 imagery.International Journal of Digital Earth,9(10):925-941[DOI:10.1080/17538947.2016.1170215]
Xie H,Luo X,Xu X,Pan H Y and Tong X H.2016b.Automated subpixel surface water mapping from heterogeneous urban environments using Landsat 8 OLI imagery.Remote Sensing,8(7):584[DOI:10.3390/rs8070584]
Xie L,Zhang H,Wang C and Chen F L.2016c.Water-body types identification in urban areas from radarsat-2 fully polarimetric SARdata.International Journal of Applied Earth Observation and Geoinformation,50:10-25[DOI:10.1016/j.jag.2016.02.012]
Xu H Q.2006.Modification of normalised difference water index(ND-WI)to enhance open water features in remotely sensed imagery.International Journal of Remote Sensing,27(14):3025-3033[DOI:10.1080/01431160600589179]
Yang X C,Zhao S S,Qin X B,Zhao N and Liang L G.2017.Mapping of urban surface water bodies from sentinel-2 MSI imagery at 10m resolution via NDWI-based image sharpening.Remote Sensing,9(6):596[DOI:10.3390/rs9060596]
Yang Y H,Liu Y X,Zhou M X,Zhang S Y,Zhan W F,Sun C and Duan Y W.2015.Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach.Remote Sensing of Environment,171:14-32[DOI:10.1016/j.rse.2015.10.005]
Yao F F,Wang C,Dong D,Luo J C,Shen Z F and Yang K H.2015.High-resolution mapping of urban surface water using ZY-3 multispectral imagery.Remote Sensing,7(9):12336-12355[DOI:10.3390/rs70912336]
Zeng C Q,Bird S,Luce J L and Wang J F.2015.A natural-rule-basedconnection(NRBC)method for river network extraction from high-resolution imagery.Remote Sensing,7(10):14055-14078[DOI:10.3390/rs71014055]
Zha L,Gong H L,Hu Z W and Du H Y.2015.The comparative research of water extraction based on high resolution imagery.Journal of Capital Normal University(Natural Science Edition),36(4):85-89(查力,宫辉力,胡卓玮,杜红悦.2015.高分影像水体信息提取对比研究.首都师范大学学报(自然科学版),36(4):85-89)[DOI:10.3969/j.issn.1004-9398.2015.04.019]
Zhang L P,Huang X,Huang B and Li P X.2006.A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery.IEEE Transactions on Geoscience and Remote Sensing,44(10):2950-2961[DOI:10.1109/TGRS.2006.876704]
Zhou Y N,Luo J C,Shen Z F,Hu X D and Yang H P.2014.Multiscale water body extraction in urban environments from satellite images.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,7(10):4301-4312[DOI:10.1109/JSTARS.2014.2360436]
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