Journal of Geo-information Science >
Research on Land Use Change Detection Based on an Object-oriented Change Vector Analysis Method
Received date: 2013-04-12
Revised date: 2013-05-21
Online published: 2014-03-10
Land cover/use change in Taizhou City is studied in this paper. Due to human or natural factors, land cover/use constantly changes. With the development of remote sensing technology, change detection is one of the important applications on resource and environment remote sensing. In this paper, we propose a Change Vector-PCA analysis method based on CVA to extract change information in the study area. First, we compute the relative difference between t1 and t2 remote sensing image of Taizhou to obtain the difference image, then employ PCA to this difference image and select principal components for the extraction of change information. Next, the image objects are obtained by multi-scale segmentation combining with the spectral and spatial characteristics and suitable segmentation scale. And, the change information is extracted automatically with the rules derived from a classification tree on the basis of Change Vector-PCA analysis of the objects. Last, we have an accuracy evaluation according samples which reaches the mapping requirement. The reason of missed or false detection is that some land cover types is significantly affected by the reason, such as farmland, grass. The result shows that Change Vector-PCA based on objects is more superior than the traditional methods based on pixel in terms of utilizing the spatial information and avoiding "salt" noises; and this method can compare several principal components transformed by multi-band difference change information. Change Vector-PCA combines the advantage of CVA and PCA. It is significant in improving the accuracy and automation level for change detection.
Key words： change vector; object-oriented; PCA; change detection
WANG Liyun, LI Yan, WANG Yuqin . Research on Land Use Change Detection Based on an Object-oriented Change Vector Analysis Method[J]. Journal of Geo-information Science, 2014 , 16(2) : 307 -313 . DOI: 10.3724/SP.J.1047.2014.00307
[1] 李德仁.摄影测量与遥感学的发展展望[J].武汉大学学报信息科学版,2008,33(12):1211-1216.
[2] Singh A. Digital change detection techniques using remotely-sensed data[J]. International Journal of Remote Sensing, 1989,10(6):989-1003.
[3] 邓小炼.基于变化矢量分析的土地利用变化检测方法研究[D].北京:中国科学院遥感应用研究所.
[4] 刘珺,田庆久,黄彦,等.2000-2010年黄淮海平原夏玉米种植时空变化检测[J].光谱学与光谱分析,2012,32(9):2534-2539.
[5] 黄慧萍.面向对象影像分析中的尺度问题研究[D].北京:中国科学院遥感应用研究所.
[6] Liu W, Theberge F, Daigle J F,et al. An efficient control of ultrashort laser filament location in air for the purpose of remote sensing[J]. Applied Physis B: Lasers and Optics, 2006,10(85):55-58.
[7] 陈阳,陈映鹰,林怡.基于面向对象分类方法的遥感影像变化检测[J].山东建筑大学学报,2008,23(6):515-521.
[8] Zhang Q,Wang J, Peng X, et al. Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data[J]. International Journal of Remote Sensing, 2002,23(15):3057-3078.
[9] 宋翠玉,李培军,杨锋杰.运用多尺度图形纹理进行城市扩展变化检测[J].国土资源遥感,2006(3):37-42.
[10] Baatz M, Schape A. Multi-resolution segmentation: An optimization approach for high quality multi-scale image segmentation[C]. // Strobl J (Eds.). Angewandte Geographische Informationsverarbeitung XII, Herbert Wichmann Verlag, 2000,12-23.
[11] 田新光.面向对象高分辨率遥感影像信息提取[D].北京:中国测绘科学研究院,2007.
[12] 刘常娟.面向对象分类方法在土地调查中的可行性研究[D].长沙:中南大学,2008.
[13] 北京天目创新科技有限公司. Defines Developer 7.0向导手册[R].北京天目创新科技有限公司,2007,21-121.
[14] 张振勇,王萍,朱鲁,等.eCognition技术在高分辨率遥感影像信息提取中的应用[J].国土资源信息化,2007(2):15-17.
[15] 王慧敏,李艳.面向对象的损毁建筑物提取[J].遥感信息,2011(117):81-85.
[16] 戴昌达,姜小光,唐伶俐.遥感图像应用处理与分析[M].北京:清华大学出版社,2004.
[17] 孙晓霞,张继贤,燕琴,等.遥感影像变化检测方法综述和展望[J].遥感信息,2011(1):119-124.
[18] Fung T, Ledrew E. Application of principal components analysis to change detection [J]. Photogrammetric Engineering and Remote Sensing, 1987,53(12):1649-1658.
[19] 林克正,班守峰,张玉双.一种基于变化向量分析的变化检测方法[J].哈尔滨理工大学学报,2008,13(4):47-49.
[20] 陈晋,何春阳,史培军,等.基于变化向量分析的土地利用/覆盖变化动态监测(I)——变化阈值确定方法[J].遥感学报,2001,5(4):259-266.
[21] Lambin E F, Strahlers A H. Change-vector analysis: A tool to detect and categorize land-cover change processes using high temporal-resolution satellite data [J]. Remote Sensing of Environment, 1994(48):231-244.
/
| 〈 |
|
〉 |