论文

卫星遥感影像中耕地信息的自动提取方法研究

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  • 1. 山东农业大学资源与环境学院, 山东 泰安 271018;
    2. 山东省土地管理局, 山东 济南 250014;
    3. 山东地勘局遥感中心, 山东 济南 250011

收稿日期: 2000-05-09

  修回日期: 2000-09-10

  网络出版日期: 2001-05-20

基金资助

山东省科委资助项目(981186103)。

Study on Automatic Abstraction Methods of Cultivated Land Information from Satellite Remote Sensing Images

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  • 1. Shandong Agricultural University, Tai'an, Shandong 271018;
    2. Land Administration Bureau of Shandong Province, Jinan, Shandong 250014;
    3. Remote Sensing Center of Geological Reconnaissance Bureau, Shandong Province, Jinan, Shandong 250011;
    4. Shandong Agricultural University, Tai'an, Shandong 271018

Received date: 2000-05-09

  Revised date: 2000-09-10

  Online published: 2001-05-20

摘要

以县(市区)为基本单位,采用遥感图像处理ENVI3.2软件和TM资料,进行了7个县(市区)10个时相的耕地信息自动提取技术研究。结果证明,选择春季时相,TM432波段合成,经几何校正和增强处理,通过交互的非监督分类提取耕地信息,可以获得满意的结果,平均分类精度达到98.22%。

本文引用格式

赵庚星, 窦益湘, 田文新, 张银辉 . 卫星遥感影像中耕地信息的自动提取方法研究[J]. 地理科学, 2001 , 21(3) : 224 -229 . DOI: 10.13249/j.cnki.sgs.2001.03.224

Abstract

Taking a county (city or district) as basic units, using remote sensing image processing software ENVI3.2 and TM data, the automatic abstraction techniques on cultivated land information of seven counties and ten temporal phases were studied. Because of the spectral characteristic difference between cultivated land subtypes, the interactive unsupervised classification method can be used for obtaining a good result. Among supervised classification methods the ENVI Spectral Angle Mapper can reach a better result because of its data purification function to maintain the typicality of training samples. Compared with salty and mountainous regions, the classification precision can be higher in inner plain region where cultivated land has comparatively accordant spectral information. Based on comparison analysis the best temporal phase of cultivated land information abstraction is in spring which is from March to May. In addition the using of non-remote sensed data such as land use map as well as expert experience is also important for promoting the precision of cultivated land acquiring.Selecting spring TM432 band composite images, processed by geometric calibration and enhancing processing, using interactive unsupervised classification method; the average precision of cultivated land classification is up to 98.22%.

参考文献

[1] 黄小虎. 怎样认识耕地总量动态平衡[J].中国土地,1998,(12):16~18.
[2] P Gong, P J Howarth. A comparison of spatial feature extraction algorithm for land-use classification with SPOT HRV data[J]. Remote Sensing of Environment, 1992, 40(2): 137-152.
[3] J R Baber, S A Briggs, V Cordon. Advances in classification for land cover mapping using SPOT HRV imagery[J]. INT. J. of Remote Sensing, 1991,12(5): 1071-1085.
[4] Peng Gong. Performance analyses of probabilistic relaxation method for land-cover classification[J]. Remote Sensing of Envir- onment, 1989,30(1): 33-42.
[5] 陈丹峰,林培,汲长远. 自组织网络与模糊规则结合在遥感土地覆盖分类中的应用研究[J]. 中国土地科学,1999,12(5):42~44.
[6] 张松岭,杨邦杰,王飞,等. 基于GIS的耕地遥感监测人机交互式图像解译系统[J]. 农业工程学报,1999,15(2):185~188.
[7] 陈宁强,戴锦芳. 人机交互式土地资源遥感解译方法研究[J]. 遥感技术与应用,1998,13(2):15~20.
[8] 戴昌达,胡德永. 应用TM图像进行1:5万县级土地资源调查制图的理论依据与关键技术. 中国农业工程研究设计院农业遥感研究室.农业遥感译文集.北京:测绘出版社,1990,11~13.
[9] 林培. 农业遥感[M]. 北京:北京农业大学出版社,1990.
[10] 王人潮. 浙江红壤资源信息系统的研制与应用[M]. 北京:中国农业出版社,1999.
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