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国土资源遥感  2001, Vol. 13 Issue (4): 40-49,67    DOI: 10.6046/gtzyyg.2001.04.07
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于目标时域散射特性的土地覆盖类型分类研究
邵芸, 范湘涛, 刘浩
中国科学院遥感所遥感信息科学重点实验室, 北京 100101
LAND COVER CLASSIFICATION BASED ON TEMPORAL BACKSCATTER SIGNATURES OF THE TARGETS
SHAO Yun, FAN Xiang-tao, LIU Hao
Lab of Remote Sensing Information Sciences, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China
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摘要 

目标散射特性随时间变化的规律称之为目标时域散射特性。目标时域散射特征是利用多时相雷达遥感图像进行目标识别的基础。本研究以广东肇庆为试验区,利用多时相单参数雷达图像进行土地覆盖类型分类研究,分析了试验区内典型植被的结构、形态及其散射机理等特征,总结了各类目标的时域散射特性,区分识别了多种目标,制作了土地覆盖类型分类图.

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孙波
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关键词 湿地动态变化 黄河流域 遥感 淮河流域 海河流域    
Abstract

This paper presents the results of a study examining the backscatter signatures of various targets as a function of time. The characteristics of a target may show seasonal changes. If so, the backscatter signatures of the target should vary as a function of time. This serves as the basis of using multi-temporal SAR images to discriminate and classify a variety of targets. This study was carried out in Zhaoqing test site in Guangdong Province of China. The geometric characteristics of various targets and their backscattering mechanism were analyzed. The temporal backscatter of targets was emphasized and the backscatter signatures of targets as a function of time were summed up. Twelve types of land cover were classified using multi-temporal Radarsat data and, in addition, a land cover map was produced based on the classification results.

Key wordsDynamic change of wetlands    Yellow River Basin    Remote sensing    Huaihe River Basin    Haihe River Basin
收稿日期: 2001-09-06      出版日期: 2011-08-02
作者简介: 邵芸(1961-),女,研究员、博士导师,获北京大学理学学士、理学硕士和中科院理学博士学位.现为中科院遥感信息科学重点实验室常务副主任.发表论文70余篇,专著4部.
引用本文:   
邵芸, 范湘涛, 刘浩. 基于目标时域散射特性的土地覆盖类型分类研究[J]. 国土资源遥感, 2001, 13(4): 40-49,67.
SHAO Yun, FAN Xiang-tao, LIU Hao . LAND COVER CLASSIFICATION BASED ON TEMPORAL BACKSCATTER SIGNATURES OF THE TARGETS. REMOTE SENSING FOR LAND & RESOURCES, 2001, 13(4): 40-49,67.
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[1] 郭华东,邵芸,廖静娟,等. 雷达对地观测理论与应用[M].北京: 科学出版社,2001a.











[2] 郭华东,邵芸,廖静娟,等. 中国雷达遥感图像分析[M].北京:科学出版社, 1999.











[3] Ahern F J, Leckie D G, Drieman J A. Seasonal changes in relative C-band backscatter of northern forest cover types[J]. IEEE Trans. Geosci. Remote Sens, 1993,31, 668-680.











[4] Brian Brisco, Ronald Brown. Principles & Applications of Imaging Radar[M](Agriculture Applications with Radar (Chapter 7)). New York: John Wiley & Sons, Inc., 1998.











[5] Campbell F, Ryerson R, Brown R. GlobeSAR: a Canadian radar remote sensing program[J]. GEOCARTO International, 1995,10(3):3-8.











[6] Ferrazzoli P, Guerriero L. Interpretation and model analysis of MAESTRO-1 Flevoland data[J]. Int J. Remote Sensing,1994,15:2901-2916.











[7] Guo Huadong, Shao Yun, Liao Jingjuan, et al.. Radar Remote Sensing in China[M]. Taylor & Francis Inc. UK, 2001b.











[8] Kasischke E S, Morrissey L, Way J B, et al.. Monitoring seasonal variations in boreal ecosystems using multi-temporal spaceborne SAR data[J]. Can. J. Remote Sens,1995d, 21, 96-109.











[9] Kurosu T, Fujita M, Chiba K. The identification of rice fields using multi-temporal ERS-1 C band SAR data[J]. Int. J. Remote Sensing, 1997,18 (14):2953-2965.











[10] Kurvonen L, Pulliainen J, Hallikainen M. Retrieval of biomass in boreal forests from multitemporal ERS-1 and JERS-1 SAR images[J]. IEEE Trans. Geosci. Remote sensing, 1999,37, 198-205.











[11] Le Toan, Laur T H, Mougin E, Lopes A. Multitemporal and dual-polarization observations of agricultural vegetation covers by X-band SAR images[J]. IEEE Trans. Geosci. Remote Sensing, 1989,27:709-717.











[12] Le Toan T, Ribbes F, Wang L F, et al.. Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results[J]. IEEE Trans. Geosci. Remote Sens., 1997,35(1):41-56.











[13] Lo C P. Principles & Applications of Imaging Radar [M] (Applications of Imaging Radar to Land Use and Land Cover Mapping (Chapter 14)). New York: John Wiley & Sons, Inc., 1998.











[14] Mcdonald A J, Bennett J C, Cookmartin G, et al.. The effect of leaf geometry on the microwave backscatter from leaves[J]. Int. J. of Remote Sens., 2000,21, 395-400.











[15] Prevot L, Dechambre M, Taconet O, et al.. Estimating the characteristics of vegetation canopies with airborne radar measurements[J]. Int. J. Remote Sensing. 1993,14:2803-2818.











[16] Pulliainen J T, Kurvonen L, Hallikainen M T. Multitemporal behavior of L- and C-band SAR observations of boreal forests[J]. IEEE Trans. Geosci. Remote sensing, 1999,927-937.











[17] Shao Yun, Guo Huadong, Liu Hao, et al.. Multi-frequency, Multi-polarization GlobeSAR Data for Land Use Mapping



[C]. Proc. First Regional GlobeSAR Workshop, 1994,32-45.











[18] Shao Yun, et al.. The GlobeSAR Data for Vegetation Discrimination, Microwave Remote Sensing for Earth Observation[J]. Science Press, 1995a,195-201.











[19] Shao Yun, Guo Huadong, Liu Hao, et al.. Effect of Polarization of GlobeSAR Data on Vegetation Discrimination[J]. GEOCARTO International, 1995b,10(3):71-76.











[20] Shao Yun, Verjee F, Staples S. Cutting through cloud[J]. GIS Asia Pacific, October/November,1997a.











[21] Shao Yun, Fan Xiangtao, Wang Cuizhen, et al.. Estimation rice growth stage using Radarsat data



[C]. Proc. IEEE IGARSS'97, 1997b,1430-1432.











[22] Shao Yun, Fan X, Liu H. SAR Technology for operational rice monitoring



[C]. Proc. IEEE IGARSS'2000, 2000, 1486-1488.











[23] Shao Y,Xiangtao Fan, et al.. Rice Monitoring and Production Estimation Using Multitemporal Radarsat [J]. Remote Sensing of Environment, 2001, 76:310-325.











[24] Tso B, Mather P M. Crop discrimination using multi-temporal SAR imagery[J]. Int. J. of Remote Sens., 1999, 20, 2443-2460.











[25] Ulaby F T, Sarabandi K, McDonald K, et al.. Michigan microwave canopy scattering mode[J]. Int. J. Remote Sensing, 1990b, 11, 1223-1253.

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