海岸水边线图像分割提取算法的数学形态学改进及其区域适应性分析
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摘要
海岸线变迁研究在海岸带研究中具有重要意义,提取遥感影像成像时刻的水边线(瞬时海岸水边线)是提取海岸线的关键。该文以海南省文昌市、琼海市2010年的Landsat TM5影像为数据源,研究多种图像阈值分割方法在海岸水边线提取中的最优算法及其区域适应性;采用多种数学形态学算子及其结构元素重构方法对结果进行碎斑块等噪声处理,分析比较在典型区域上的表现力;最后从阈值选取难易程度、提取海岸水边线的精度、误差来源机理方面进行整体评价分析。结果表明:改进的归一化差异水体指数(Modified Normalized Difference Water Index,MNDWI)在海岸水边线提取中显示了明显的优势,主要表现在较好的海陆对比度差异、较少的噪声影响以及在复杂环境下的区域普适性等方面;采用3×3结构元素的数学形态学开运算进行后处理,在保证海岸水边线位置精度的基础上,可对近海鱼塘、山体阴影以及反射特性比较弱的植被等造成的误提碎斑块进行合理去除;河口及淤泥质岸线、人工养殖区及生物质岸线处是图像阈值分割法岸线提取的主要偏差来源区域。
Coastline transition study is of great importance in coastal zone research.Extracting the waterlines(transient coastlines,the boundary between the sea and the land at certain time in an image)using remote sensing images is the key for extracting coastlines.Based on the Landsat TM5 image of 2010 in Wenchang and Qionghai City,Hainan Province,we have studied and compared the optimal algorithms for waterline extraction by using various image threshold segmentation methods.The mathematical morphology operator and structural element reconstruction methods were used to deal with the noises such as irrelevant patches.The waterlines extracted by different algorithms in typical areas are compared.The accuracy of the extracted waterlines was evaluated and discussed in the aspects of optimal threshold selection,waterlines position offset errors and causation of deviation errors.The results suggested that the MNDWI(modified normalized difference water index)algorithm has distinct advantages in waterline extraction,especially in providing better sea and land contrast,less irrelevant noise effects and regional applicability in complex environment.The 3×3 window of opening operation of mathematical morphology showed better performance for post-processing to ensure the accuracy of waterline positions,especially in removing the false patches caused by fishponds,mountain shadows and vegetation with low reflectivity.The type of estuaries and silt coastlines,artificial coastlines(such as offshore aquaculture)are the main regions that cause position deviation.The study will contribute to analysis of coastline historical transitions,evaluation of coastal zone ecological and humanistic environment,and the protection of the coastal environment.
Coastline transition study is of great importance in coastal zone research.Extracting the waterlines(transient coastlines,the boundary between the sea and the land at certain time in an image)using remote sensing images is the key for extracting coastlines.Based on the Landsat TM5 image of 2010 in Wenchang and Qionghai City,Hainan Province,we have studied and compared the optimal algorithms for waterline extraction by using various image threshold segmentation methods.The mathematical morphology operator and structural element reconstruction methods were used to deal with the noises such as irrelevant patches.The waterlines extracted by different algorithms in typical areas are compared.The accuracy of the extracted waterlines was evaluated and discussed in the aspects of optimal threshold selection,waterlines position offset errors and causation of deviation errors.The results suggested that the MNDWI(modified normalized difference water index)algorithm has distinct advantages in waterline extraction,especially in providing better sea and land contrast,less irrelevant noise effects and regional applicability in complex environment.The 3×3 window of opening operation of mathematical morphology showed better performance for post-processing to ensure the accuracy of waterline positions,especially in removing the false patches caused by fishponds,mountain shadows and vegetation with low reflectivity.The type of estuaries and silt coastlines,artificial coastlines(such as offshore aquaculture)are the main regions that cause position deviation.The study will contribute to analysis of coastline historical transitions,evaluation of coastal zone ecological and humanistic environment,and the protection of the coastal environment.
引文
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[17]王琳,徐涵秋,李胜.厦门岛及其邻域海岸线变化的遥感动态监测[J].遥感技术与应用,2005,20(4):404-410.
[18]李猷,王仰麟,彭建,等.深圳市1978年至2005年海岸线的动态演变分析[J].资源科学,2009,31(5):875-883.
[19]吴小娟,肖晨超,崔振营,等.“高分二号”卫星数据面向对象的海岸线提取法[J].航天返回与遥感,2015,36(4):84-92.
[20]张怡,李晓敏,马毅,等.基于遥感的珠江口海岸线变迁分析[J].海洋测绘,2014,34(3):52-55.
[21]杨智翔.利用遥感技术监测江苏海岸线变迁与滩涂围垦[J].人民黄河,2013,35(1):85-87.
[22]陈正华,毛志华,陈建裕.利用4期卫星资料监测1986~2009年浙江省大陆海岸线变迁[J].遥感技术与应用,2011,26(1):68-73.
[23] ALESHEIKH A A,GHORBANALI A,NOURI N.Coastline change detection using remote sensing[J].International Journal of Environmental Science&Technology,2007,4(1):61-66.
[24]孙美仙,张伟.福建省海岸线遥感调查方法及其应用研究[J].应用海洋学学报,2004,23(2):213-218.
[25] 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.
[26]巢子豪,高一博,李彦辉,等.基于遥感和GIS的海州湾海岸线提取方法研究[J].淮海工学院学报(自然科学版),2014,30(4):87-91.
[27]杨存建,徐美.遥感信息机理的水体提取方法的探讨[J].地理研究,1998,17(增刊):86-89.
[28] OUMA Y O,TATEISHI R.A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes:An empirical analysis using Landsat TM and ETM+data[J].International Journal of Remote Sensing,2006,27(15-16):3153-3181.
[29]瞿继双,王超,王正志.一种基于多阈值的形态学提取遥感图象海岸线特征方法[J].中国图象图形学报,2003,8(7):805-809.
[30] KOHLER R.A segmentation based on thresholding[J].Computer Vision,Graphics and Image Processing,1981,15:319-338.
[31] SAHOO P K,CHEN Y C.A survey of thresholding techniques[J].Computer Vision,Graphics and Image Processing,1988,41:233-260.
[32] OTSU N.A threshold selection method from gray-level histogram[J].IEEE Transactions on Systems Man&Cybernetics,1979,9(1):62-66.
[33]朱长明,张新,骆剑承,等.基于样本自动选择与SVM结合的海岸线遥感自动提取[J].国土资源遥感,2013,25(2):69-74.
[34] LIU H G,JEZE K C.Automated extraction of coastline from satellite imagery by integrating canny edge detection and locally adaptive thresholding methods[J].International Journal of Remote Sensing,2004,25(5):937-958.
[35]席晓燕,沈楠,李小娟.ETM+影像水体提取方法研究[J].计算机工程与设计,2009,30(4):993-996.
[36]徐涵秋.利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J].遥感学报,2005,9(5):589-595.
[37]毕海芸,王思远,曾江源,等.基于TM影像的几种常用水体提取方法的比较和分析[J].遥感信息,2012,27(5):77-82.
[2]张霞,庄智.秦皇岛市海岸线遥感提取及变化监测[J].遥感技术与应用,2014,29(4):625-630.
[3]赵宗泽,刘荣杰,马毅,等.近30年来湄洲湾海岸线变迁遥感监测与分析[J].海岸工程,2013,32(1):19-27.
[4]谭李春.基于多源数据的广西大陆海岸线变迁研究[J].测绘与空间地理信息,2013,36(8):72-75.
[5]李行,张连蓬,姬长晨,等.基于遥感和GIS的江苏省海岸线时空变化[J].地理研究,2014,33(3):414-426.
[6]黎良财,LU D S,张晓丽,等.基于遥感的1987-2013年北部湾海岸线变迁研究[J].海洋湖沼通报,2015,37(4):132-142.
[7]国家质量技术监督局.GB/T 18190-2000,海洋学术语·海洋地质学[S].北京:中国标准出版社,2000.
[8]高燕,周成虎,苏奋振,等.基于多特征的人工海岸线提取方法[J].测绘工程,2014,23(5):1-5.
[9]刘云朋,王建梅,楼立志.基于ALOS影像与CV模型的瞬时海岸线提取[J].测绘与空间地理信息,2014,37(2):91-95.
[10]于杰,陈国宝,黄梓荣,等.近10年间广东省3个典型海湾海岸线变迁的遥感分析[J].海洋湖沼通报,2014,36(3):91-96.
[11]栗云召,于君宝,韩广轩,等.基于遥感的黄河三角洲海岸线变化研究[J].海洋科学,2012,36(4):99-106.
[12]严海兵,李秉柏,陈敏东.遥感技术提取海岸线的研究进展[J].地域研究与开发,2009,28(1):101-105.
[13]于杰,杜飞雁,陈国宝,等.基于遥感技术的大亚湾海岸线的变迁研究[J].遥感技术与应用,2009,24(4):512-516.
[14]于永海,苗丰民,王玉广,等.基于3S技术的海岸线测量与管理应用研究[J].地理与地理信息科学,2003,19(6):24-27.
[15]孙才志,李明昱.辽宁省海岸线时空分布及驱动因素分析[J].地理与地理信息科学,2010,26(3):63-67.
[16]张明,蒋雪中,张俊儒,等.遥感影像海岸线特征提取研究进展[J].人民黄河,2008,30(6):7-9.
[17]王琳,徐涵秋,李胜.厦门岛及其邻域海岸线变化的遥感动态监测[J].遥感技术与应用,2005,20(4):404-410.
[18]李猷,王仰麟,彭建,等.深圳市1978年至2005年海岸线的动态演变分析[J].资源科学,2009,31(5):875-883.
[19]吴小娟,肖晨超,崔振营,等.“高分二号”卫星数据面向对象的海岸线提取法[J].航天返回与遥感,2015,36(4):84-92.
[20]张怡,李晓敏,马毅,等.基于遥感的珠江口海岸线变迁分析[J].海洋测绘,2014,34(3):52-55.
[21]杨智翔.利用遥感技术监测江苏海岸线变迁与滩涂围垦[J].人民黄河,2013,35(1):85-87.
[22]陈正华,毛志华,陈建裕.利用4期卫星资料监测1986~2009年浙江省大陆海岸线变迁[J].遥感技术与应用,2011,26(1):68-73.
[23] ALESHEIKH A A,GHORBANALI A,NOURI N.Coastline change detection using remote sensing[J].International Journal of Environmental Science&Technology,2007,4(1):61-66.
[24]孙美仙,张伟.福建省海岸线遥感调查方法及其应用研究[J].应用海洋学学报,2004,23(2):213-218.
[25] 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.
[26]巢子豪,高一博,李彦辉,等.基于遥感和GIS的海州湾海岸线提取方法研究[J].淮海工学院学报(自然科学版),2014,30(4):87-91.
[27]杨存建,徐美.遥感信息机理的水体提取方法的探讨[J].地理研究,1998,17(增刊):86-89.
[28] OUMA Y O,TATEISHI R.A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes:An empirical analysis using Landsat TM and ETM+data[J].International Journal of Remote Sensing,2006,27(15-16):3153-3181.
[29]瞿继双,王超,王正志.一种基于多阈值的形态学提取遥感图象海岸线特征方法[J].中国图象图形学报,2003,8(7):805-809.
[30] KOHLER R.A segmentation based on thresholding[J].Computer Vision,Graphics and Image Processing,1981,15:319-338.
[31] SAHOO P K,CHEN Y C.A survey of thresholding techniques[J].Computer Vision,Graphics and Image Processing,1988,41:233-260.
[32] OTSU N.A threshold selection method from gray-level histogram[J].IEEE Transactions on Systems Man&Cybernetics,1979,9(1):62-66.
[33]朱长明,张新,骆剑承,等.基于样本自动选择与SVM结合的海岸线遥感自动提取[J].国土资源遥感,2013,25(2):69-74.
[34] LIU H G,JEZE K C.Automated extraction of coastline from satellite imagery by integrating canny edge detection and locally adaptive thresholding methods[J].International Journal of Remote Sensing,2004,25(5):937-958.
[35]席晓燕,沈楠,李小娟.ETM+影像水体提取方法研究[J].计算机工程与设计,2009,30(4):993-996.
[36]徐涵秋.利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J].遥感学报,2005,9(5):589-595.
[37]毕海芸,王思远,曾江源,等.基于TM影像的几种常用水体提取方法的比较和分析[J].遥感信息,2012,27(5):77-82.
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