Please wait a minute...
 
国土资源遥感  2014, Vol. 26 Issue (4): 34-40    DOI: 10.6046/gtzyyg.2014.04.06
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于选权迭代估计与非监督分类的多光谱图像变化检测
李莎, 倪维平, 严卫东, 吴俊政, 张晗
西北核技术研究所, 西安 710024
Change detection of multi-spectral images based on iterative estimation with weight selection and unsupervised classification
LI Sha, NI Weiping, YAN Weidong, WU Junzheng, ZHANG Han
Northwest Institute of Nuclear Technology, Xi'an 710024, China
全文: PDF(1400 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

针对多光谱图像的变化检测问题,提出了一种基于选权迭代估计(iterative estimation with weight selection,IEWS)与非监督分类(unsupervised classification,UC)的多光谱图像变化检测方法。借鉴IEWS的思想,并以类似于迭代加权多元变化检测(iteratively reweighted multivariate alteration detection,IRMAD)的迭代模式进行回归估计,得到初步的变化检测结果; 并通过对初始变化信息的UC处理,以及对不同类别的IEWS,得到最终的变化检测结果。利用该方法对TM图像进行了实验,结果表明: 所得到的变化信息在空间位置上同该区域相应时间段内土地利用/覆盖的变化情况具有很好的一致性; 同时与多元变化检测及IRMAD方法变化检测的结果相比较,表明该方法对相对较小的变化信息具有更好的变化检测能力。

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
刘惠颖
郭华东
张露
关键词 海冰分类合成孔径雷达(SAR)密集度    
Abstract

To solve the change detection problem of multi-channel remote sensing images, this paper proposes a method based on iterative estimation with weight selection (IEWS) and unsupervised classification (UC). Firstly, the primary change information is obtained according to the concept of IEWS, and the iteration scheme of the estimation is also similar to that of the iteratively re-weighted multivariate alteration detection (IRMAD). And then, the primary change information is classified by the UC and processed by the IEWS, which can get the eventual change information. The experimental results with multi-spectral data indicate that the method proposed in this paper is effective. By using this method, the spatial coherence between the change information and the change of land use/cover in this area is good. As for the detection of change in small regions, the method is especially obviouely better than the commonly-used methods of multivariate alteration detection (MAD) and IRMAD.

Key wordssea ice classification    synthetic aperture Radar(SAR)    ice concentration
收稿日期: 2013-09-18      出版日期: 2014-09-17
:  TP751.1  
作者简介: 李莎(1984-),女,助理研究员,主要研究方向为遥感图像处理与仿真等。Email:lisha@nint.ac.cn。
引用本文:   
李莎, 倪维平, 严卫东, 吴俊政, 张晗. 基于选权迭代估计与非监督分类的多光谱图像变化检测[J]. 国土资源遥感, 2014, 26(4): 34-40.
LI Sha, NI Weiping, YAN Weidong, WU Junzheng, ZHANG Han. Change detection of multi-spectral images based on iterative estimation with weight selection and unsupervised classification. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(4): 34-40.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2014.04.06      或      https://www.gtzyyg.com/CN/Y2014/V26/I4/34

[1] 赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003:241-242. Zhao Y S.The Theory and Method of Remote Sensing Application and Analysis[M].Beijing:Science Press,2003:241-242.

[2] 张继贤,杨贵军.单一时相遥感数据土地利用与覆盖变化自动检测方法[J].遥感学报,2005(3):294-299. Zhang J X,Yang G J.Automatic land use and land cover change detection with one temporary remote sensing image[J].Journal of Remote Sensing,2005(3):294-299.

[3] Sing A.Digital change detection techniques using remotely-sensed data[J].International Journal of Remote Sensing,1989,10:989-1003.

[4] Mouat D A,Mahin G C,Lancaster J.Remote sensing techniques in the analysis of change detection[J].Geocarto International,1993,8(2):39-50.

[5] Arzandeh S,Wang J.Monitoring the change of phragmites and distribution using satellite data[J].Canadian Journal of Remote Sensing,2003,29(1):24-35.

[6] Li D R.Remotely sensed images and GIS data fusion for automatic change detection[J].International Journal of Image and Data Fusion,2010,1(1):99-108.

[7] Kwarteng A Y,Chavez P S.Change detection study of Kuwait City and environs using multi-temporal Landsat Thematic Mapper data[J].International Journal of Remote Sensing,1998,19(3):1651- 1661.

[8] 张辉,王建国.一种基于主分量分析的SAR图像变化检测算法[J].电子与信息学报,2008,30(7):1728-1730. Zhang H,Wang J G.A SAR image change detection algorithm based on principal component analysis[J].Journal of Electronics and Information Technology,2008,30(7):1728-1730.

[9] Nielsen A A,Conradsen K,Simpson J J.Multivariate alteration detection(MAD)and MAF postprocessing in multispectral,bitemporal image data:New approaches to change detection studies[J].Remote Sensing of Environment,1998,64(1):1-19.

[10] Canty M J,Nielsenb A A,Schmidtc M.Automatic radiometric normalization of multi-temporal satellite imagery[J].Remote Sensing of Environment,2004,91(3/4):441-451.

[11] Canty M J,Nielsen A A.Visualization and unsupervised classification of changes in multispectral satellite imagery[J].International Journal of Remote Sensing,2006,27(18):3961-3975.

[12] 廖明生,朱攀,龚健雅.基于典型相关分析的多元变化检测[J].遥感学报,2000,4(3):197-201. Liao M S,Zhu P,Gong J Y.Multivariate change detection based on canonical transformation[J].Journal of Remote Sensing,2000,4(3):197-201.

[13] 陈垒,马润赓,申维.基于典型相关分析的遥感影像变化检测[J].地质通报,2007,26(7):916-920. Chen L,Ma R G,Shen W.Detection of remote sensing image alteration based on canonical correlation analysis[J].Geological Bulletin of China,2007,26(7):916-920.

[14] Nielsen A A.The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data[J].IEEE Transactions on Image Processing,2007,16(2):463-478.

[15] Canty M J,Nielsen A A.Automatic radiometric normalization of multi-temporal satellite imagery with the iteratively re-weighted MAD transformation[J].Remote Sensing of Environment,2008,112(3):1025-1036.

[16] 邱卫宁,陶本藻,姚宜斌,等.测量数据处理理论与方法[M].武汉:武汉大学出版社,2008:123-145. Qiu W N,Tao B Z,Yao Y B,et al.The Theory and Method of Surveying Data Processing[M].Wuhan:Wuhan University Press,2008:123-145.

[17] Marpu P R,Gamba P,Canty M J.Change detection using iteratively reweighted regression with neural networks[C]//2010 IEEE International Geoscience and Remote Sensing Symposium.Honolulu,HI:IEEE,2010:2563-2566.

[18] 顾有林,韩帮春.快鸟数据在光学遥感器成像仿真中的应用[J].系统仿真学报,2008,20(22):6265-6267. Gu Y L,Han B C.Application of high resolution quickbird satellite data in imaging simulation of optical remote sensor[J].Journal of System Simulation,2008,20(22):6265-6267.

[1] 江珊, 王春, 宋宏利, 刘玉锋. 基于SAR与光学遥感数据相结合的农作物种植类型识别研究[J]. 国土资源遥感, 2020, 32(4): 105-110.
[2] 刘惠颖, 郭华东, 张露. 基于HJ-1CSAR数据的辽东湾海冰分类[J]. 国土资源遥感, 2014, 26(3): 125-129.
[3] 王晓华, 邓喀中, 杨化超. 集成互补不变特征的SAR影像自动配准[J]. 国土资源遥感, 2014, 26(1): 52-56.
[4] 巩彪, 黄韦艮, 陈鹏. 基于改进的归一化Hough变换的ASAR图像船只尾迹检测[J]. 国土资源遥感, 2012, 24(3): 33-37.
[5] 高帅, 牛铮, 刘晨洲. 基于RADARSAT SAR估测热带人工林叶面积指数研究[J]. 国土资源遥感, 2008, 20(4): 35-38.
[6] 杜培军, 孙敦新, 林卉. 窗口大小对SAR图像滤波效果的影响分析[J]. 国土资源遥感, 2006, 18(2): 12-15.
[7] 林卉, 杜培军, 舒宁, 赵长胜. INSAR干涉图滤波处理[J]. 国土资源遥感, 2004, 16(3): 33-36.
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