基于改进的亚像元分解方法的高光谱海岸瞬时水边线提取
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摘要
海岸线是多年平均大潮高潮所形成的海水和陆地分界线的痕迹线,遥感技术可以提供大范围的海岸线动态监测。传统的硬分类方法提取海岸瞬时水边线是基于像元级的基础上进行的,其提取的精度较低;然而利用亚像元分解方法在复杂海岸地带上提取海岸瞬时水边线,是一项既新颖又具有挑战性的任务。因此,提出一种改进的亚像元海岸瞬时水边线提取方法(Improved Sub-pixel Coastal Waterline,ISPCW)可以获得较高的海岸瞬时水边线提取精度。首先,使用了一种水体-植被-不透水层-土壤模型(Water-VegetationImpervious-Soil,W-V-I-S)用于检测和确定海岸地带的W-V-I-S混合像元和纯净端元光谱;随后使用全约束最小二乘法(Fully Constrained Least Squares,FCLS)估计W-V-I-S混合像元中水体丰度值;最后使用空间吸引力模型获得海岸瞬时水边线。在上海实验区中,采用EO-1高光谱数据,将ISPCW方法和传统的多端元光谱混合分析(Multiple Endmember Spectral Mixture Analysis,MESMA)、混合调谐匹配滤波法(Mixture Tuned Matched Filtering,MTMF)、连续最大角凸锥(Sequential Maximum Angle Convex Cone,SMACC)、能量约束最小化(Constrained Energy Minimization,CEM)混合像元分解方法和归一化水体指数(Normalized Difference Water Index,NDWI)进行对比。实验结果表明,ISPCW方法用于提取海岸瞬时水边线获得较好的效果,其精度达到0.38个像元,与MESMA、MTMF、SMACC、CEM和NDWI方法相比,精度分别提高了22.4%、33.3%、42.4%、43.2%和51.3%,可以更有效的应用于高光谱海岸瞬时水边线提取。
Shoreline is described as an intersection of coastal land and water surface indicating water edge movements as the tides rise and fall. Remote sensing technology can provide a wide range dynamicmonitoring of the shoreline. However,traditional hard classification methods are mainly used to extract coastal waterline at the pixel level,and achieve the low accuracy. Whereas sub-pixel coastal waterline extraction is an attractive and challenging task due to the complex features in the coastal region. Therefore,an improved subpixel coastal waterline extraction method(ISPCW) is presented to achieve the higher accuracy of coastal waterline extraction. Firstly,a Water-Vegetation-Impervious-Soil(W-V-I-S) model is presented to detect WV-I-S mixed pixels and determine endmember spectrum in the coastal region. Secondly,the linear spectral mixture unmixing technique based on Fully Constrained Least Squares(FCLS) is applied to the W-V-I-S mixed pixels for water abundance estimation; and finally,spatial attraction model is used to extract coastal waterline. In the experiment performed on EO-1 Hyperion data of Shanghai study area,Multiple Endmember Spectral Mixture Analysis(MESMA),Mixture Tuned Matched Filtering(MTMF),Sequential Maximum Angle Convex Cone(SMACC),and Constrained Energy Minimization(CEM),and classical Normalized Difference Water Index(NDWI) methods are chosen for the coastal waterline extraction comparison. The results indicate that the proposed ISPCW method achieved the best accuracy of 0. 38 pixels in the experiment,and the accuracy of ISPCW method improved by 22. 4%,33. 3%,42. 4%,43. 2%,and 51. 3% compared with MESMA,MTMF,SMACC,CEM,and NDWI methods,respectively. Therefore,from these results,the ISPCW method exhibits better performance for coastal waterline extraction than the traditional pixel level method and sub-pixel level method,and can be effectively applied to coastal waterline extraction in the coastal region
Shoreline is described as an intersection of coastal land and water surface indicating water edge movements as the tides rise and fall. Remote sensing technology can provide a wide range dynamicmonitoring of the shoreline. However,traditional hard classification methods are mainly used to extract coastal waterline at the pixel level,and achieve the low accuracy. Whereas sub-pixel coastal waterline extraction is an attractive and challenging task due to the complex features in the coastal region. Therefore,an improved subpixel coastal waterline extraction method(ISPCW) is presented to achieve the higher accuracy of coastal waterline extraction. Firstly,a Water-Vegetation-Impervious-Soil(W-V-I-S) model is presented to detect WV-I-S mixed pixels and determine endmember spectrum in the coastal region. Secondly,the linear spectral mixture unmixing technique based on Fully Constrained Least Squares(FCLS) is applied to the W-V-I-S mixed pixels for water abundance estimation; and finally,spatial attraction model is used to extract coastal waterline. In the experiment performed on EO-1 Hyperion data of Shanghai study area,Multiple Endmember Spectral Mixture Analysis(MESMA),Mixture Tuned Matched Filtering(MTMF),Sequential Maximum Angle Convex Cone(SMACC),and Constrained Energy Minimization(CEM),and classical Normalized Difference Water Index(NDWI) methods are chosen for the coastal waterline extraction comparison. The results indicate that the proposed ISPCW method achieved the best accuracy of 0. 38 pixels in the experiment,and the accuracy of ISPCW method improved by 22. 4%,33. 3%,42. 4%,43. 2%,and 51. 3% compared with MESMA,MTMF,SMACC,CEM,and NDWI methods,respectively. Therefore,from these results,the ISPCW method exhibits better performance for coastal waterline extraction than the traditional pixel level method and sub-pixel level method,and can be effectively applied to coastal waterline extraction in the coastal region
引文
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[26]CHANG C I,PLAZA A.A fast iterative algorithm for implementation of pixel purity index[J].IEEE Geoscience and Remote Sensing Letters,2006,3(1):63-67.
[27]YANG C H,EVERITT J H,BRADFORD J M.Yield estimation from hyperspectral imagery using spectral angle mapper(SAM)[J].Transactions of the ASABE,2008,51(2):729-737.
[28]XU X,ZHONG Y F,ZHANG L P.A sub-pixel mapping method based on an attraction model for multiple shifted remotely sensed images[J].Neurocomputing,2014,134:79-91.
[29]THIELER E R,HIMMELSTOSS E A,ZICHICHI J L,et al.The digital shoreline analysis system(DSAS)version 4.0-an Arc GIS extension for calculating shoreline change[R].Reston:U.S.Geological Survey,2009.
[30]张兵,孙旭.高光谱图像混合像元分解[M].北京:科学出版社,2015.ZHANG B,SUN X.The mixed pixel unmixing of hyperspectral images[M].Beijing:Science Press,2015.
[31]寻丽娜,方勇华,李新.基于CEM的高光谱图像小目标检测算法[J].光电工程,2007,34(7):18-21.XUN L N,FANG Y H,LI X.Target detection algorithm in hyperspectral image based on CEM[J].Opto-Electronic Engineering,2007,34(7):18-21.
[32]XU Q H.Modification of normalised difference water index(NDWI)to enhance open water features in remotely sensed imagery[J].International Journal of Remote Sensing,2006,27(14):3025-3033.
[2]林桂兰.遥感在海岸线修测中的应用技术探讨[J].福建水产,2005(3):59-62,90.LIN G L.Application of remote-sensing technique in coastline surveying[J].Journal of Fujian Fisheries,2005(3):59-62,90.
[3]MUJABAR P S,CHANDRASEKAR N.Shoreline change analysis along the coast between Kanyakumari and Tuticorin of India using remote sensing and GIS[J].Arabian Journal of Geosciences,2013,6(3):647-664.
[4]LI R,KEONG C W,RAMCHARAN E,et al.A coastal GIS for shoreline monitoring and management-case study in Malaysia[J].Surveying and Land Information Systems,1998,58(3):157-166.
[5]LI R,DI K C,MA R J.3-D shoreline extraction from IKONOS satellite imagery[J].Marine Geodesy,2003,26(1/2):107-115.
[6]GENS R.Remote sensing of coastlines:detection,extraction and monitoring[J].International Journal of Remote Sensing,2010,31(7):1819-1836.
[7]RAHMAN A F,DRAGONI D,EL-MASRI B.Response of the Sundarbans coastline to sea level rise and decreased sediment flow:A remote sensing assessment[J].Remote Sensing of Environment,2011,115(12):3121-3128.
[8]BOAK E H,TURNER I L.Shoreline definition and detection:a review[J].Journal of Coastal Research,2005,21(4):688-703.
[9]BOUCHAHMA M,YAN W L.Monitoring shoreline change on Djerba Island using GIS and multi-temporal satellite data[J].Arabian Journal of Geosciences,2014,7(9):3705-3713.
[10]杜涛,张斌.用小波技术分析遥感图像确定岸线位置的研究[J].海洋科学,1999,23(4):19-21.DU T,ZHANG B.A study of mapping coast by processing remote sensing image with wavelets[J].Marine Science,1999,23(4):19-21.
[11]NUNZIATA F,MIGLIACCIO M,LI X F,et al.Coastline extraction using dual-polarimetric COSMO-Sky Med Ping Pong mode SAR data[J].IEEE Geoscience and Remote Sensing Letters,2014,11(1):104-108.
[12]PEKEL J F,COTTAM A,GORELICK N,et al.High-resolution mapping of global surface water and its long-term changes[J].Nature,2016,540(7633):418-422.
[13]FENG Y J,LIU Y,LIU D.Shoreline mapping with cellular automata and the shoreline progradation analysis in Shanghai,China from 1979 to 2008[J].Arabian Journal of Geosciences,2015,8(7):4337-4351.
[14]张汉女.基于SVM的海岸线提取方法研究[D].长春:东北师范大学,2010.ZHANG H N.Coastline extraction using support vector machine from remote sensing image[D].Changchun:Northeast Normal University,2010.
[15]MCFEETERS S K.The use of the Normalized Difference Water Index(NDWI)in the delineation of open water features[J].International Journal of Remote Sensing,1996,17(7):1425-1432.
[16]FRANKLE J,ROBERTS D A,HALLIGAN K,et al.Hierarchical Multiple Endmember Spectral Mixture Analysis(MESMA)of hyperspectral imagery for urban environments[J].Remote Sensing of Environment,2009,113(8):1712-1723.
[17]SANKEY T,GLENN N.Landsat-5 TM and lidar fusion for sub-pixel juniper tree cover estimates in a western rangeland[J].Photogrammetric Engineering&Remote Sensing,2011,77(12):1241-1248.
[18]SHAH C A.Automated lake shoreline mapping at subpixel accuracy[J].IEEE Geoscience and Remote Sensing Letters,2011,8(6):1125-1129.
[19]XIE H,LUO X,XU X,et al.Automated subpixel surface water mapping from heterogeneous urban environments using landsat 8 OLI imagery[J].Remote Sensing,2016,8(7):584.
[20]刘善伟,张杰,马毅,等.遥感与DEM相结合的海岸线高精度提取方法[J].遥感技术与应用,2011,26(5):613-618.LIU S W,ZHANG J,MA Y,et al.Coastline extraction method based on remote sensing and DEM[J].Remote Sensing Technology and Application,2011,26(5):613-618.
[21]BARRY P.EO-1/Hyperion science data user’s guide,level1_B[R].Redondo Beach,CA:TRW Space,Defense and Information Systems,2001:555-557.
[22]施雅飞,李小春.基于SLEVTH模型的长江口北岸土地利用演化模拟研究[J].现代测绘,2010,33(3):6-8.SHI Y F,LI X C.Land use dynamic evolution simulation of the north branch of Yangtze river estuary based on SLEUTH model[J].Modern Surveying and Mapping,2010,33(3):6-8.
[23]RIDD M K.Exploring a V-I-S(vegetation-impervious surfacesoil)model for urban ecosystem analysis through remote sensing:comparative anatomy for cities[J].International Journal of Remote Sensing,1995,16(12):2165-2185.
[24]ZHA Y,GAO J,NI S.Use of normalized difference built-up index in automatically mapping urban areas from TM imagery[J].International Journal of Remote Sensing,2003,24(3):583-594.
[25]WOLF A F.Using Worldview-2 Vis-NIR multispectral imagery to support land mapping and feature extraction using normalized difference index ratios[C]//Proc.SPIE 8390,Algorithms and Technologies for Multispectral,Hyperspectral,and Ultraspectral Imagery XVIII,83900N.Baltimore,Maryland,United States:SPIE,2012.
[26]CHANG C I,PLAZA A.A fast iterative algorithm for implementation of pixel purity index[J].IEEE Geoscience and Remote Sensing Letters,2006,3(1):63-67.
[27]YANG C H,EVERITT J H,BRADFORD J M.Yield estimation from hyperspectral imagery using spectral angle mapper(SAM)[J].Transactions of the ASABE,2008,51(2):729-737.
[28]XU X,ZHONG Y F,ZHANG L P.A sub-pixel mapping method based on an attraction model for multiple shifted remotely sensed images[J].Neurocomputing,2014,134:79-91.
[29]THIELER E R,HIMMELSTOSS E A,ZICHICHI J L,et al.The digital shoreline analysis system(DSAS)version 4.0-an Arc GIS extension for calculating shoreline change[R].Reston:U.S.Geological Survey,2009.
[30]张兵,孙旭.高光谱图像混合像元分解[M].北京:科学出版社,2015.ZHANG B,SUN X.The mixed pixel unmixing of hyperspectral images[M].Beijing:Science Press,2015.
[31]寻丽娜,方勇华,李新.基于CEM的高光谱图像小目标检测算法[J].光电工程,2007,34(7):18-21.XUN L N,FANG Y H,LI X.Target detection algorithm in hyperspectral image based on CEM[J].Opto-Electronic Engineering,2007,34(7):18-21.
[32]XU Q H.Modification of normalised difference water index(NDWI)to enhance open water features in remotely sensed imagery[J].International Journal of Remote Sensing,2006,27(14):3025-3033.
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