Please wait a minute...
img

官方微信

遥感技术与应用  2013, Vol. 28 Issue (3): 526-532    DOI: 10.11873/j.issn.1004-0323.2013.3.526
遥感应用     
山东省寿光市滨海地区盐田信息提取方法研究
郝利娜1,2,张志2,张翠芬1,3
(1.中国地质大学(武汉)信息工程学院,湖北 武汉 430074;
2.中国地质大学(武汉)地球科学学院,湖北 武汉 430074;
3.山东女子学院信息技术学院,山东 济南 250000)
Information Extraction Method of the Salt Field in the Coastal Areas in Shouguang,Shandong
Hao Lina1,2,Zhang Zhi2,Zhang Cuifen1,3
(1.Faculty of Information Engineering,China,University of Geosciences,Wuhan 430074,China;
2.Faculty of Earth Sciences,China University of Geosciences,Wuhan 430074,China;
3.Faculty of Information Technology,Shandong Womens University,Jinan 250000,China)
 全文: PDF(4542 KB)  
摘要:

山东省寿光市滨海地区盐田水体因含盐度高,其光谱特征与海域水体及其他地物差异大,光谱特征显著;盐田系人为建造,排列整齐\,几何特征明显,遥感影像上表现为纹理特征显著(棋盘状纹理、条纹状纹理),纹理指标可计算性强。首先采用缨帽变换方法增强光谱信息,采用定向滤波及灰度共生矩阵方法增强纹理信息;其次基于增强的光谱与纹理信息,采用以面向应用为目的的感兴趣地物提取方法对研究区TM图像进行分类,将分类结果与仅依据纯光谱及仅依据纯纹理分类结果相对比,分类总体精度分别为90.8985%、84.9102%和60.4017%。结果表明:以面向应用为目的的感兴趣地物提取方法分类精度最高。

关键词: 盐田光谱特征纹理特征缨帽变换定向滤波灰度共生矩阵    
Abstract:

Because of the high salinity,the spectral features of the salt field in the coastal areas in Shouguang of Shandong are greatly different from marine water bodies and other ground objects spectral features; the salt field is man-made,so,it has regular arrangement and significant geometric characteristics.In the remote sensing images,the above characteristics are shown as significant texture features (chessboard-like texture and stripe-like texture) and the texture indices have an excellent computability.In this paper,Firstly,using the tasseled cap transform to enhance spectral features of the salt field and using the directional convolution and the gray level co-occurrence matrix to enhance the texture features of the salt field.Secondly,based on the enhanced spectral and texture features,the TM image of the study area is classified by using the application-oriented interested ground objects extraction.The classification result is compared with the classification results based on spectrum only or texture only,the overall accuracy is respectively 90.8985%,84.9102% and 60.4017%.The result indicates that the accuracy of the classification method proposed in this paper is the highest.

Key words: Salt field    Spectral features    Texture features    Tasseled cap transform    Directional convolution    Gray level co-occurrence matrix
收稿日期: 2012-05-31 出版日期: 2013-07-05
:  TP 79  
基金资助:

山东省高等学校科技计划项目(J12LN42)、山东省信息化与工业化融合专项(2012EI070)联合资助。

通讯作者: 张志(1964-),男,湖北鄂州人,教授,博士生导师,主要从事遥感地质及陆表遥感研究。Email:zhangz6402@126.com。    
作者简介: 郝利娜(1982-),女,陕西渭南人,博士研究生,主要从事遥感地质及环境遥感研究。Email:madingludejin@163.com。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
郝利娜
张志
张翠芬

引用本文:

郝利娜,张志,张翠芬. 山东省寿光市滨海地区盐田信息提取方法研究[J]. 遥感技术与应用, 2013, 28(3): 526-532.

Hao Lina,Zhang Zhi,Zhang Cuifen. Information Extraction Method of the Salt Field in the Coastal Areas in Shouguang,Shandong. Remote Sensing Technology and Application, 2013, 28(3): 526-532.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2013.3.526        http://www.rsta.ac.cn/CN/Y2013/V28/I3/526

[1]Li Shusheng,Zhao Shufang,Zhou Xiuyun,et al.Environmental Analysis on Salt Field in Northern China,Changlu[J].Tianjin Science&Technology,2011,(5):114-116.[李树生,赵淑芳,周秀云,等.中国北方长芦海盐区盐田环境现状分析[J].科学观察,2011,(5):114-116.]

[2]Peng Wanglu,Bai Zhenping,Liu Xiangnan,et al.Introduction to Remote Sensing[M].Beijing:Higher Education Press,2002.[彭望琭,白振平,刘湘南,等.遥感概论[M].北京:高等教育出版社,2002.]

[3]Chica-Olmo M,Arbarca-Hernandez F.Computing Geostatistical Image Texture for Remotely Sensed Data Classification[J].Computers & Geosciences,2000,26(4):373-383.

[4]Xu Jiandong,Luan Peng,Fan Xiaoying,et al.Analysis of Spectrum and Texture Information on Changbaishan Tianchi Volcano Caldera and Its Application[J].Seismology and Geology,2009,31(4):607-616.[许建东,栾鹏,樊笑英,等.基于遥感影像光谱与纹理分析的地物分类——以长白山天池火山地区为例[J].地震地质,2009,31(4):607-616.]

[5]Ren Xianyi,Zhang Jialin,Chen Chaoyang,et al.Segmenting TextureImages Using Texture Spectrum Method [J].Journal of Image and Graphics,1998,3(12):983-986.[任仙怡,张佳林,陈朝阳,等.基于纹理谱的纹理分割方法[J].中国图象图形学报,1998,3(12):983-986.]

[6]Shu Ning.Remote Sensing Image Texture Analysis and Fractal Assessment [J].Journal of Wuhan Technical University of Surveying and Mapping,1998,23(4):370-373.[舒宁.卫星遥感影像纹理分析与分形分维方法[J].武汉测绘科技大学学报,1998,23(4):370-373.]

[7]Jiang Qingxiang,Liu Huiping.Extracting TM Image Information Using Texture Analysis [J].Journal of Remote Sensing,2004,8(5):458-464.[姜青香,刘慧平.利用纹理分析方法提取TM图像信息[J].遥感学报,2004,8(5):458-464.]

[8]Wang Huanping,Liu Yong.The Study on Classification of High Resolution Remote Sensing Image in Saline Area based on Window Fourier Transform Power Spectrum Analysis[J].Remote Sensing Technology and Application,2011,26(2):233-238.[王焕萍,刘勇.基于窗口傅立叶变换功率分析的盐田地区高分辨率遥感影像分割分类方法探讨[J].遥感技术与应用,2011,26(2):233-238.]

[9]Gao Chengcheng,Hui Xiaowei.GLCM-based Texture Feature Extraction [J].Computer Systems & Applications,2010,19(6):195-198.[高程程,惠晓威.基于灰度共生矩阵的纹理特征提取[J].计算机系统应用,2010,19(6):195-198.]

[10]Haralick R M,Shanmugam K,Dinstein I.Textural Features for Image Classification[J].IEEE Transactions on Systems,Man and Cybernetics,1973,3(6):610-621.

[11]Cui Bingde.Remote Sensing Image Classification based on SVM Classifier [J].Computer Engineering and Applications,2011,47(27):189-191.[崔炳德.支持向量机分类器遥感图像分类研究[J].计算机工程与应用,2011,47(27):189-191.]

[12]Dixon B,Candade N.Multispectral Landuse Classification Using Neural Networks and Support Vector Machines:One or the Other,or Both? [J].International Journal of Remote Sensing,2008,29(4):1185-1206.

[13]Li Panchi,Xu Shaohua.Support Vector Machine and Kernel Function Characteristic Analysis in Pattern Recognition[J].Computer Engineering and Design,2005,26(2):302-304.[李盼池,许少华.支持向量机在模式识别中的核函数特性分析[J].计算机工程与设计,2005,26(2):302-304.]

[14]Farid M,Lorenzo B.Classification of Hyperspectral Remote Sensing Images with Support Vector Machines[J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(8):1778-1790.

[15]Foody G M,Mather A.A Relative Evaluation of Multiclass Image Classification by Support Vector Machines[J].IEEE Transactions on Geoscience and Remote Sensing,2004,6(42):1335-1343.

[16]Pal M,Mather P M.Support Vector Machines for Classification in Remote Sensing[J].International Journal of Remote Sensing,2005,3(26):1007-1011.

[1] 韩涛,潘剑君,张培育,曹罗丹. Sentinel-2A与Landsat-8影像在油菜识别中的差异性研究[J]. 遥感技术与应用, 2018, 33(5): 890-899.
[2] 高莎,林峻,马涛,吴建国,郑江华. 新疆巴音布鲁克草原马先蒿光谱特征提取与分析[J]. 遥感技术与应用, 2018, 33(5): 908-914.
[3] 杨朦朦,汪汇兵,欧阳斯达,范奎奎,戚凯丽. 基于双树复小波分解的BP神经网络遥感影像分类[J]. 遥感技术与应用, 2018, 33(2): 313-320.
[4] 刘剑锋,张喜旺. 基于光谱和时相特征的夏玉米遥感识别[J]. 遥感技术与应用, 2016, 31(6): 1131-1139.
[5] 翟玮,沈焕锋,黄春林. 结合PolSAR影像纹理特征分析提取倒塌建筑物[J]. 遥感技术与应用, 2016, 31(5): 975-982.
[6] 马东辉,柯长青. 南京冬季典型植被光谱特征分析[J]. 遥感技术与应用, 2016, 31(4): 702-708.
[7] 邓滢,张红,王超,刘萌. 结合纹理与极化分解的面向对象极化SAR水体提取方法[J]. 遥感技术与应用, 2016, 31(4): 714-723.
[8] 赵玉,王红,张珍珍. 基于遥感光谱和空间变量随机森林的黄河三角洲刺槐林健康等级分类[J]. 遥感技术与应用, 2016, 31(2): 359-367.
[9] 刘吉凯,钟仕全,梁文海. 基于多时相Landsat8 OLI影像的作物种植结构提取[J]. 遥感技术与应用, 2015, 30(4): 775-783.
[10] 林楚彬,李少青. 基于热红外像元分解的裸土信息自动提取方法[J]. 遥感技术与应用, 2014, 29(6): 1067-1073.
[11] 邹亚荣,黄磊,张治平. 结合纹理特征的SVM岛礁信息提取分析[J]. 遥感技术与应用, 2014, 29(5): 812-817.
[12] 安如,姜丹萍,李晓雪,王喆,Jonathan Arthur Quaye-Ballard . 基于地面实测高光谱数据的三江源中东部草地植被光谱特征研究[J]. 遥感技术与应用, 2014, 29(2): 202-211.
[13] 刘萌萌,刘亚岚,孙国庆,彭立. 结合纹理特征的SVM样本分层土地覆盖分类[J]. 遥感技术与应用, 2014, 29(2): 315-323.
[14] 杨凯,沈渭寿,刘波,欧阳琰. 那曲典型草地植被光谱特征分析[J]. 遥感技术与应用, 2014, 29(1): 40-45.
[15] 潘佩芬,杨武年,戴晓爱,郑菠. 不同森林植被的高光谱特征分析[J]. 遥感技术与应用, 2013, 28(6): 1000-1005.