Please wait a minute...
 
国土资源遥感  2014, Vol. 26 Issue (2): 121-127    DOI: 10.6046/gtzyyg.2014.02.20
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
基于Bayes决策的机载全极化SAR图像滑坡信息提取
王兴玲1, 胡德勇2, 唐宏3, 舒阳3
1. 民政部国家减灾中心, 北京 100124;
2. 首都师范大学资源环境与旅游学院, 北京 100048;
3. 北京师范大学减灾与应急管理研究院, 北京 100875
Extraction of landslide information from airborne polarimetric SAR images based on Bayes decision theory
WANG Xingling1, HU Deyong2, TANG Hong3, SHU Yang3
1. National Disaster Reduction Center of China, Ministry of Civil Affairs, P R China, Beijing 100124, China;
2. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;
3. Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
全文: PDF(1736 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

将雷达遥感技术应用于滑坡灾害调查是地学应用的一个重要方向,特别是在多云多雨地区。由中国科学院电子学研究所研发的高效能机载SAR系统(high-performance airborne synthetic aperture Radar system,HASARS)具备X波段双基线干涉和P波段全极化观测的能力,是国内首家多频段多模式机载SAR系统。从多极化机载SAR数据的特征选择和信息提取等角度,评估了不同极化模式组合对滑坡信息提取精度的影响;并基于Bayes决策理论,提出了多极化SAR图像分类的特征选择方法。利用不同研究样区的特征选择结果提取了多个滑坡的范围,提取精度均在90%以上。HASARS的高空间分辨率及其获取的高精度DEM和P波段全极化观测,可以近实时、高精度地获取地表滑坡灾害专题信息,在滑坡等减灾救灾领域具有广阔的应用前景。

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
蒋金豹
张玲
崔希民
蔡庆空
孙灏
关键词 植被覆盖区土壤水分反演water-cloud模型    
Abstract

The application of the Radar remote sensing data to landslide investigation is of great importance, especially in cloudy and rainy areas. The high-performance airborne synthetic aperture Radar system(HASARS) developed by Institute of Electronics,Chinese Academy of Sciences,is the first home-made system characterized by multi-band and multi-mode,which has the capability of interferometric survey of X band and double antennas as well as polarimetric observation of P band. In this paper, the accuracies of landslide information extraction from polarimetric SAR data using different polarization combinations were investigated to evaluate the technology, methodology and implementation ideas of the landslide applications with the HASARS, and the focuses included two aspects: the methods of information extraction and the ways to select the feature. The results show that, based on Bayes decision theory and using the samples of landslide and non-landslide in the image to analyze and make decision, the method of feature selection could make classification of polarimetric SAR image satisfactorily. Based on the results of feature selection, the authors extracted the landslide regions from SAR images with supervised classification methods, with their accuracies higher than 90%. The airborne SAR system, with high spatial resolution, high precision DEM production and P band polarimetric observations, can obtain the thematic information of landslide surface more flexibly and precisely, and hence it has a broad prospect in the landslide disaster relief applications.

Key wordsvegetation-covered area    soil moisture    inversion    water-cloud model
收稿日期: 2013-04-01      出版日期: 2014-03-28
:  TP751.1  
基金资助:

国家自然科学基金项目“InSAR支持下基于支持向量机的地震滑坡空间预测研究”(编号:40901227)和国家863计划项目“高效能航空SAR遥感应用系统——减灾救灾应用示范”(编号:2007AA120306)共同资助。

通讯作者: 胡德勇,Email:deyonghu@cnu.edu.cn。
作者简介: 王兴玲(1973- ),男,博士,研究员,主要从事遥感减灾应用及卫星运行管理方面的研究。Email:wangxingling@ndrcc.gov.cn。
引用本文:   
王兴玲, 胡德勇, 唐宏, 舒阳. 基于Bayes决策的机载全极化SAR图像滑坡信息提取[J]. 国土资源遥感, 2014, 26(2): 121-127.
WANG Xingling, HU Deyong, TANG Hong, SHU Yang. Extraction of landslide information from airborne polarimetric SAR images based on Bayes decision theory. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 121-127.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2014.02.20      或      https://www.gtzyyg.com/CN/Y2014/V26/I2/121
[1] 胡德勇,李京,陈云浩,等.GIS支持下滑坡灾害空间预测方法研究[J].遥感学报,2007,11(6):852-859. Hu D Y,Li J,Chen Y H,et al.GIS-based landslide spatial prediction methods:A case study in Comeron highland,Malaysia[J].Journal of Remote Sensing,2007,11(6):852-859.
[2] 廖明生,唐婧,王腾,等.高分辨率SAR数据在三峡库区滑坡监测中的应用[J].中国科学:地球科学,2012,42(2):217-229. Liao M S,Tang J,Wang T,et al.Landslide monitoring with high resolution SAR data in the three Gorges region[J].Scientia Sinica Terrae,2012,42(2):217-229.
[3] 刘菊,廖静娟,沈国状.基于全极化SAR数据反演鄱阳湖湿地植被生物量[J].国土资源遥感,2012,24(3):38-43. Liu J,Liao J J,Shen G Z.Retrieval of wetland vegetation biomass in Poyang lake based on quad- polarization image[J].Remote Sensing for Land and Resources,2012,24(3):38-43.
[4] 王庆,曾琪明,廖静娟.基于极化分解的极化特征参数提取与应用[J].国土资源遥感,2012,24(3):103-110. Wang Q,Zeng Q M,Liao J J.Extraction and application of polarimetric characteristic parameters based on polarimetric decomposition[J].Remote Sensing for Land and Resources,2012,24(3):103-110.
[5] 吴永辉,计科峰,郁文贤.基于H-α和改进C-均值的全极化SAR图像非监督分类[J].电子与信息学报,2007,29(1):30-34. Wu Y H,Ji K F,Yu W X.Unsupervised classification of fully polarimetric SAR image using H-α decomposition and modified C-mean algorithm[J].Journal of Electronics and Information Technology,2007,29(1):30-34.
[6] Qi Z X,Yeh A G O,Li X,et al.A novel algorithm for land use and land cover classification using Radarsat-2 polarimetric SAR data[J].Remote Sensing of Environment,2012,118:21-39.
[7] Wen X B,Zhang H,Zhang J G,et al.Multiscale modeling for classification of SAR imagery using hybrid EM algorithm and genetic algorithm[J].Progress in Natural Science,2009,19(8):1033-1036.
[8] Balaguer A,Ruiz L A,Hermosilla T,et al.Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification[J].Computers and Geosciences,2010,36(2):231-240.
[9] 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. Hu D Y,Li J,Chen Y H,et al.Water and settlement area extraction from single-band,single-polarization SAR images based on SVM method[J].Journal of Image and Graphics,2008,13(2):257-263.
[10] 吴永辉,计科峰,郁文贤.SVM全极化SAR图像分类中的特征选择[J].信号处理,2007,23(6):877-881. Wu Y H,Ji K F,Yu W X.A new feature selection algorithm for SVM-based fully polarimetric SAR image classification[J].Signal Processing,2007,23(6):877-881.
[11] Bhanu B,Lin Y Q.Genetic algorithm based feature selection for target detection in SAR images[J].Image and Vision Computing,2003,21(7):591-608.
[12] Li S,Wu H,Wan D S,et al.An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine[J].Knowledge-Based Systems,2011,24(1):40-48.
[13] 刘蓉,靳红梅,段福庆.基于Bayes决策的光谱分类[J].光谱学与光谱分析,2010,30(3):838-841. Liu R,Jin H M,Duan F Q.Spectral classification based on Bayes decision[J].Spectroscopy and Spectral Analysis,2010,30(3):838-841.
[14] 刘国庆,熊红,黄顺吉,等.多视极化合成孔径雷达图象的分类和极化通道优化[J].电子科学学刊,1998,20(1):56-61. Liu G Q,Xiong H,Huang S J,et al.Classification of multi-look polarimetric SAR imagery and polarization channel optimization[J].Journal of Electronics and Information Technology,1998,20(1):56-61.
[15] 岳晋,杨汝良,宦若虹.贝叶斯理论在多波段SAR图像融合分类中的应用[J].中国科学院研究生院学报,2008,25(2):257-263. Yue J,Yang R L,Huan R H.Application of Bayesian theory in multiband SAR image fusion for classification[J].Journal of the Graduate School of the Chinese Academy of Sciences,2008,25(2):257-263.
[16] 杨文,颜卫,涂尚坦,等.基于贝叶斯信息准则的极化干涉SAR图像非监督分类[J].电子与信息学报,2012,34(11):2628-2634. Yang W,Yan W,Tu S T,et al.An unsupervised classification method of polinSAR image based on Bayesian information criterion[J].Journal of Electronics and Information Technology,2012,34(11):2628-2634.
[17] Hospedales T M,Vijayakumar S.Structure inference for Bayesian multisensor scene understanding[J].IEEE Transactions on pattern analysis and machine intelligence,2008,30(12):2140-2157.
[1] 高琪, 王玉珍, 冯春晖, 马自强, 柳维扬, 彭杰, 季彦桢. 基于改进型光谱指数的荒漠土壤水分遥感反演[J]. 自然资源遥感, 2022, 34(1): 142-150.
[2] 孙一鸣, 张宝钢, 吴其重, 刘奥博, 高超, 牛静, 何平. 国产微景一号小卫星影像的城市裸地识别应用[J]. 自然资源遥感, 2022, 34(1): 189-197.
[3] 艾璐, 孙淑怡, 李书光, 马红章. 光学与SAR遥感协同反演土壤水分研究进展[J]. 自然资源遥感, 2021, 33(4): 10-18.
[4] 沙永莲, 王晓文, 刘国祥, 张瑞, 张波. 基于SBAS InSAR的新疆哈密砂墩子煤田开采沉陷监测与反演[J]. 自然资源遥感, 2021, 33(3): 194-201.
[5] 杜程, 李得林, 李根军, 杨雪松. 基于高原盐湖光谱特性下的溶解氧反演应用与探讨[J]. 自然资源遥感, 2021, 33(3): 246-252.
[6] 宋承运, 胡光成, 王艳丽, 汤超. 基于表观热惯量与温度植被指数的FY-3B土壤水分降尺度研究[J]. 国土资源遥感, 2021, 33(2): 20-26.
[7] 范嘉智, 罗宇, 谭诗琪, 马雯, 张弘豪, 刘富来. 基于FY-3C/MWRI的湖南省地表温度遥感反演评价[J]. 国土资源遥感, 2021, 33(1): 249-255.
[8] 杨立娟. 基于两层随机森林模型估算中国东部沿海地区的PM2.5浓度[J]. 国土资源遥感, 2020, 32(4): 137-144.
[9] 石海岗, 梁春利, 张建永, 张春雷, 程旭. 岸线变迁对田湾核电站温排水影响遥感调查[J]. 国土资源遥感, 2020, 32(2): 196-203.
[10] 马振宇, 陈博伟, 庞勇, 廖声熙, 覃先林, 张怀清. 基于林火特征分类模型的森林火情等级制图[J]. 国土资源遥感, 2020, 32(1): 43-50.
[11] 杨崇, 刘国祥, 于冰, 张波, 张瑞, 王晓文. 基于InSAR形变的辽河油田曙光采油厂储层参数反演[J]. 国土资源遥感, 2020, 32(1): 209-215.
[12] 贺军亮, 韩超山, 韦锐, 周智勇, 东启亮. 基于偏最小二乘的土壤重金属镉间接反演模型[J]. 国土资源遥感, 2019, 31(4): 96-103.
[13] 封红娥, 李家国, 朱云芳, 韩启金, 张宁, 田淑芳. GF-1与Landsat8水体叶绿素a浓度协同反演——以太湖为例[J]. 国土资源遥感, 2019, 31(4): 182-189.
[14] 左家旗, 王泽根, 边金虎, 李爱农, 雷光斌, 张正健. 地表不透水面比例遥感反演研究综述[J]. 国土资源遥感, 2019, 31(3): 20-28.
[15] 樊宪磊, 阎宏波, 瞿瑛. 基于HJ-1A/B CCD地表反照率估算方法比较与验证[J]. 国土资源遥感, 2019, 31(3): 123-131.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
版权所有 © 2015 《自然资源遥感》编辑部
地址:北京学院路31号中国国土资源航空物探遥感中心 邮编:100083
电话:010-62060291/62060292 E-mail:zrzyyg@163.com
本系统由北京玛格泰克科技发展有限公司设计开发