海岸带遥感微尺度信息及其组合挖掘提取和方法应用研究

杨晓梅; 龚剑明; 高振宇

海岸带遥感微尺度信息及其组合挖掘提取和方法应用研究

中国科学院地理科学与资源研究所, 资源与环境信息系统国家重点实验室, 北京, 100101

基金项目: 国家“八六三”项目(2009AA12Z148);资源与环境信息系统国家重点实验室自主创新团队计划(088RA400SA)

计量
- 文章访问数: 728
- HTML全文浏览量: 16
- PDF下载量: 794
- 被引次数: 0
出版历程
- 收稿日期: 2009-01-05
- 修回日期: 2009-03-09

The research on extracting method of microscale remote sensing information combination and application in coastal zone

State Key Laboratory of Resource and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

摘要

摘要: 基于海岸带高分辨率信息需求理论支持下的信息挖掘技术,面对我国海岸带可持续发展的需求,以中高分辨率遥感影像为数据源,以滩涂、水边线、海堤、养殖场等海岸带地物为专题信息挖掘提取实例,建立了"像元→基元→目标"的识别方法体系,针对面向对象的信息提取分析方法进行研究。即首先通过采用光谱和形状相结合的分割算法来获取内部特征相对均一的一系列基元对象,再通过对基元对象的典型特征进行分析和判别来实现目标提取。结果表明,该方法是可行的,它提高了遥感影像信息的识别精度,为动态性很强的海岸带地物信息挖掘提取提供了研究思路,在海岸带监测、管理、开发和利用,编制现实性很强的海岸带专题图等应用领域展现了该研究示例的科学性和实际意义。
- 面向对象 /
- 图像分割 /
- 海岸带 /
- 信息提取
Abstract: Due to the need of rapid and sustainable development in China's coastal zones, the high-resolution information theory using data mining technology becomes an urgent research focus.However, the traditional pixel-based image analysis methods cannotmeet the needs of this development trend.Attempt is to present an information extraction approach in terms of image segmentation based on an object-orientedal-gorithm for high-resolution remote sensing images.The aim of research is to establish an identification system of "pixel-primitive-object".Through extraction and combination of microscale coastal zone features, some objects are classified or recognized, e.g., beach, coast line, sea wall, and mariculture pond.First, various internal characteristics of relatively homogeneous primitive objects are extracted using an image segmentation algorithm based on both spectral and shape information.Second, the features of those primitives are analyzed to ascertain an optimal object by adopting certain feature rules.Results indicate that the model is practical to realize and the extraction accuracy of the coastal information is significantly improved compared with the traditional approaches.Therefore, this model provides a potential way to serve highly dynamic coastal zones for monitoring, management, development and utilization.
- object-oriented /
- image segmentation /
- coastal zone /
- information extraction

HTML全文

参考文献(27)

潘德炉,王迪峰.我国海洋光学遥感应用科学研究的新进展[J].地球科学进展,2004,19(4):506-512.

杨晓梅,杜云艳.陈秀法.中国海岸带高分辨率遥感系统技术基础研究[J].海洋学报,2003,25(6):61-68.

苏奋振,周成虎,杨晓梅,等.海洋地理信息系统理论基础及其关键技术研究[J].海洋学报,2004,26(6):22-28.

杜云艳,杨晓梅,王敬贵.中国海岸带及近海多源数据空间组合和运行的基础研究[J].海洋学报,2003,25(5):38-48.

BISCHOF H,SCHNEIDER W,PINZ A J.Multi-spectral classification of Landsat images using neural networks[J].IEEE Transactions on Geo-science and Remote Sensing,1992(30):482-490.

FOODY G.Image classification with a neural network:from completely-crisp to fully-fuzzy situations[M]//ATKINSON Tate,Ad vances in Remote Sensing and GIS Analysis.Chichester:Wiley & Son,1999:17-37.

熊桢,童庆禧,郑兰芬.用于高光谱遥感图象分类的一种高阶神经网络算法[J].中国图象图形学报,2000,5(3):196-201.

ZHUANG Xin-hua,HUANG Yan,ZHAO Yu-xin.Gaussion mixture density modelling,decomposition,and applications[J].IEEE Transactions on Image Processing,1996,5(9):1293-1301.

DEMPSTER A P,LAIRD N M,RUBIN D B.Maximum likelihood estimation from incomplete data via EM algorithm[J].J R Statist Soc,1977(39):1-38.

REDNER R A,WALKER H F.Mixture densities,maximum likelihood and the EM algorithm[J].SIAM Review,1984,26(2):195-239.

VAPNIK V N.The Nature of Statistical Learning Theory[M].New York:Springer-Verlag,1995:35-85.

LOBO A,CHIC O,CASTERAD A.Classification of Mediterranean crops with muhisensor data:per-pixel versus per-object statistics and image segmentation[J].International Journal of Remote Sensing,1996(17):2358-2400.

BLASCHKE T,HAY G J.Object-oriented image analysis and scale-space:theory and methods for modeling and evaluating muhiscale landscape structures[J].International Archives of Photogrammetry and Remote Sensing,2001,34(4):22-29.

BAATZ M,SCH PE A.Multiresolution segmentation-an optimization approach for high quality multi-scale image segmentation[M]//STROBL J,BLASCHKE T,GRlESEBNER G.Angewandte Geographische Informations-Verarbeitung Ⅻ.Karlsruhe:Wichmann Verlag,2000:12-23.

BENZ U,BAATZ M,SCHREIER G.OSCAR-object oriented segmentation and classification of advanced radar allow automated infor mation extraction[C]//Proceedings of International Geoscience and Remote Sensing Symposium(IGARss),Hawaii,USA,v4.2001:1913-1915.

CASEY C,MAGGI K,FAITH R K,et al.Classification of the wildland urban interface:a comparison of pixel-and object-based classi-fications using high-resolution aerial photography[J].Computers,Environment and Urban Systems,2008(32):317-326.

URSULA C B.PETER H,GREGOR W,et al.Multi-resolution,object-oriented fuzzy analysis of remote sensing data for GIS-ready in formation[J].Journal of Photogrammetry & Remote Sensing,2004(58):239-258.

Definiens Image Inc.Definiens in earth sciences[O/L].http://www.definiens-imaging.com,Oline:2007-12-30.

BLASCHKE T,STROBL J.WHAT's wrong with pixels? Some recent developments interfacing remote sensing and GIS[J].GeoBIT/GIS,2001(6):12-17.

AFLIN P,ATKINSON P,CURRAN P.Per-field classification of land use using the forth-coming very fine resolution satellite sensors:problems and potential solutions[M]//ATKINSON Tate.Advances in Remote Sensing and GIS Analysis.Chichester:Wiley & Son,1999:219-239.

HILL P R,CANAGARAJAH C N,BULL D R.Image segmentation using a texture gradient based watershed transform[J].IEEE Trans on Image Processing,2003,12(12):1618-1633.

YANG Xiao-mei,LAN Rong-qiu,LUO Jian-chneg.Quantizing and analyzing the feature information of coastal zone based on high-resolution remote sensing image[J].Acta Oceanologica Sinica,2006,25(6):33-42.

田艳琴,郭平,卢汉清.基于灰度共生矩阵的多波段遥感图像纹理特征的提取[J].计算机科学,2004,31(12):162-163.

冯建辉,杨玉静.基于灰度共生矩阵提取纹理特征图像的研究[J].北京测绘,2007(3):19-22.

李金莲,刘晓玫,李恒鹏.SPOT5影像纹理特征提取与土地利用信息识别方法[J].遥感学报,2006,10(6):926-931.

许玮.卢凌.基于3S的大型水上桥梁识别系统[J].武汉理工大学学报(交通科学与工程版),2007,31(1):35-3 8.

胡德勇,李京,陈云浩,等.单波段单极化SAR图像水体和居民地信息提取方法研究[J].中国图象图形学报,2008,13(2):257-263.

施引文献

资源附件(0)

访问统计