Please wait a minute...
浙江大学学报(工学版)
J4  2013, Vol. 47 Issue (8): 1508-1516    DOI: 10.3785/j.issn.1008-973X.2013.08.027
地质工程     
资源一号“02C”遥感影像土地利用分类
马利刚1, 张乐平1, 邓劲松1, 汪雅婕2, 王珂1
1.浙江大学 遥感与信息技术应用研究所,浙江 杭州 310029；2.浙江省第二测绘院,浙江 杭州 310012
Land use classification using ZY1-“02C” remote sensing images
MA Li-gang1, ZHANG Le-ping1, DENG Jing-song1, WANG Ya-jie2, WANG Ke1
1. Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310029, China; 2. The 2nd Surveying and Mapping Institute of Zhejiang, Hangzhou 310012, China
 全文: PDF  HTML
摘要：

以杭州市主城区为试验区,针对建设用地与裸地空间纹理的复杂度和水体与阴影高程差异,拟采用半方差函数与Z检验结合选出的图像纹理结合高程信息等分量实现神经网络分类.结果表明,与单纯使用光谱信息相比,图像纹理的引入使总体分类精度提高约4%,加入高程信息则可以使总体分类精度提高约10%,达到82.75%,表明该方法可以应用于新数据的分类并得到相对满意的结果.
Abstract:

Land use of Hang Zhou city was classified from ZY-1 02C imagery in an neutral network approach using spectral, texture, and nDSM (Normalized Digital Surface Model) features. Texture features are selected from the combined use of semi-variance function and Z test. Construction and bare land were separated according to texture complexity distinction. Shadow and water were identified with the support of nDSM . Accuracy assessment indicate that addition of image textures can improve overall classification accuracy by 4% in comparison with classification using original bands solely. Furthermore, inclusion of elevation data can increase overall accuracy by 10% to 82.78%, which demonstrates the effectiveness of proposed method in the classification of 02C data. Classification result is acceptable.
出版日期: 2013-08-01
:  TP 751.1  
基金资助:

国家自然科学基金资助项目（31172023)；国家高技术产业化应用专项资助项目（2009214);浙江省重点科技创新团队资助项目（2010R50030);
通讯作者: 王珂，男，教授.     E-mail: kwang@zju.edu.cn
作者简介: 马利刚(1986—)，男，博士，从事遥感应用研究. E-mail: 11114054@zju.edu.cn
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　　

引用本文:

马利刚, 张乐平, 邓劲松, 汪雅婕, 王珂. 资源一号“02C”遥感影像土地利用分类[J]. J4, 2013, 47(8): 1508-1516.

MA Li-gang, ZHANG Le-ping, DENG Jing-song, WANG Ya-jie, WANG Ke. Land use classification using ZY1-“02C” remote sensing images. J4, 2013, 47(8): 1508-1516.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2013.08.027    或    http://www.zjujournals.com/eng/CN/Y2013/V47/I8/1508

［1］ 国土资源部.资源一号”02C”在轨交付仪式举行［EB/OL］.［2012-04-18］.http:∥www.mlr.gov.cn/xwdt/tpxw/201204/t20120418_1085506.htm.

［2］ DENG Sheng-lu, QI Hao-wen. A survey of image classification methods and techniques for improving classification performance ［J］. International Journal of Remote Sensing, 2007, 28(5): 823-870.

［3］ DENG Sheng-lu, QI Hao-wen.. Extraction of urban impervious surfaces from an IKONOS image ［J］. International Journal of Remote Sensing, 2009, 30(5): 1297-1311.

［4］ VAN DE VOORDE T, JACQUET W, CANTERS F. Mapping form and function in urban areas: An approach based on urban metrics and continuous impervious surface data ［J］. Landscape and Urban Planning, 2011, 102(3): 143-155.

［5］ ZHU Zhe, WOODCOCK C E, ROGAN J, et al. Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data ［J］. Remote Sensing of Environment, 2012, 117: 72-82.

［6］ WU Bo, WANG Xiao-qin, SHEN Huan-feng, et al. Feature selection based on max-min-associated indices for classification of remotely sensed imagery ［J］. International Journal of Remote Sensing, 2012, 33(17): 5492-5512.

［7］ IHADL. ZY-1 “02C” Satellite parameters［EB/OL］.［2012-03-26］http:∥blog.csdn.net/ihadl/article/details/7395366

［8］ NIKOLAKOPOULOS K G. Comparison of four different fusion techniques for IKONOS data.［C］∥ IEEE International Geoscience and Remote Sensing Symposium. New York: IEEE, 2004, 2534-2537.

［9］ 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.

［10］ 韩凝.空间信息在面向对象分类方法中的应用［D］. 浙江大学, 2011.

HAN Ning. Application of spatial information in object-based classification: A case study on delineating Torreya using IKONOS imagery［D］Hangzhou: Zhejiang University, 2011: 5253

［11］ DENG Sheng-lu, QI Hao-wen. Urban classification using full spectral information of Landsat ETM+ imagery in Marion County, Indiana ［J］. Photogrammetric Engineering & Remote Sensing, 2005, 71(11): 1275-1284.

［12］ SHANMUGAN K S, NARAYANAN V, FROST V S, et al. Textural features for Dadar image-analysis ［J］. IEEE Transactions on Geoscience and Remote Sensing, 1981, 19(3): 153-156.

［13］ PACIFICI F, CHINI M, EMERY W J. A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification ［J］. Remote Sensing of Environment, 2009, 113(6): 1276-1292.

［14］ THAPA R B, MURAYAMA Y. Urban mapping, accuracy, & image classification: A comparison of multiple approaches in Tsukuba City, Japan ［J］. Applied Geography, 2009, 29(1): 135-144.

［15］ 吴宏安,蒋建军,张海龙,等. 比值居民地指数在城镇信息提取中的应用 ［J］. 南京师大学报：自然科学版. 2006(3): 118-121.

WU Hong-an, JIANG Jian-jun, ZHANG Hai-long et al. Application of ratio residential-area index to retrieve urban residential areas based on Landsat TM data ［J］Journal of Nan Jing Normal University: Natural Science, 2006,（3.）: 118-121.
[1] 厉小润, 朱洁尔, 王晶, 赵辽英. 组合核支持向量机高光谱图像分类[J]. J4, 2013, 47(8): 1403-1410.
[2] 赖小波,朱世强,方纯洁. 一种复杂背景图像三维重建算法及其医学应用[J]. J4, 2012, 46(11): 2061-2067.
[3] 车红昆, 吕福在, 项占琴. 基于顺序向前浮动搜索时频优选特征的缺陷识别[J]. J4, 2011, 45(12): 2235-2239.
[4] 韩凝, 张秀英, 王小明, 陈利苏, 王珂. 高分辨率影像香榧树分布信息提取[J]. J4, 2010, 44(3): 420-425.