Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2010, Vol. 22 Issue (4) : 91-97     DOI: 10.6046/gtzyyg.2010.04.19
Technology Application |
Water Area Extraction and Change Detection of the Poyang Lake Using SAR Data
 WANG Qing 1,2, LIAO Jing-juan 1
1. Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; 2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China
Download: PDF(887 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

 In order to extract the open water from the Poyang Lake by applying SAR data in different periods, the authors firstly analyzed the scattering mechanism of water,vegetation and sand,then used the texture of SAR imagery and polarized ratio and polarized difference to enhance the description of targets, and employed principal component transformation to enhance the contrast of water and other objects. With the first component,the area of open water on SAR imagery could be effectively and accurately extracted by setting up a proper threshold. The SAR images used were Envisat-ASAR and ALOS-PALSAR alternating polarization mode data. Meanwhile,after the analysis of two kinds of sample data with Jeffries-Matusita distance,ASAR data with C band could provide more accurate extraction of open water than those of PALSAR with L band. Finally,the open water of the Poyang Lake was extracted from SAR imagery in spring,summer and winter,and the changes of water area indicated the regular change of the open water of the Poyang Lake.

Keywords Remote sensing image      Rock information      The best density separation method     
: 

TP 79

 
Issue Date: 02 August 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
WU De-wen
ZHANG Yuan-fei
ZHU Gu-chang
Cite this article:   
WU De-wen,ZHANG Yuan-fei,ZHU Gu-chang. Water Area Extraction and Change Detection of the Poyang Lake Using SAR Data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(4): 91-97.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.04.19     OR     https://www.gtzyyg.com/EN/Y2010/V22/I4/91

[1]丁莉东,余文华,覃志豪,等. 基于MODIS的鄱阳湖区水体水灾遥感影像图制作[J].国土资源遥感,2007(1):82-85. 

[2]于欢,张树清,李晓峰,等. 基于TM影像的典型内陆淡水湿地水体提取研究[J]. 遥感技术与应用,2008,23(3):310-315.

[3]都金康,黄永胜,冯学智,等. SPOT卫星影像的水体提取方法及分类研究[J]. 遥感学报,2001,5(3):214-219.

[4]孙涛,黄诗峰. Envisat ASAR在特大洪涝灾害监测中的应用[J]. 南水北调与水利科技,2006,4(2):33-35.

[5]廖静娟,沈国状. 基于多极化SAR图像的鄱阳湖湿地地表淹没状况动态变化分析[J].遥感技术与应用,2008,23(4):373-377.

[6]沈国状,廖静娟. 面向对象技术用于多极化SAR图像地表淹没程度自动探测分析[J].遥感技术与应用,2007,22(1):79-82.

[7]朱俊杰,郭华东,范湘涛,等. 基于纹理与成像知识的高分辨率SAR图像水体检测[J].水科学进展,2006,17(4):525-530.

[8]殷悦,宫辉力,赵文吉. 基于SAR影像的洪水淹没范围信息提取的研究[J].测绘与空间地理信息,2007,30(4):50-54.

[9]郑伟,刘闯,曹云刚,等. 基于Asar与TM图像的洪水淹没范围提取[J].测绘科学,2007,32(5):180-181.

[10]ESA. BEST-Basic Envisat SAR Toolbox[EB/OL]. [2009-02-10]. http://earth.esa.int/best/software/.

[11]Frost S V,Stiles A J,Shanmugan S K,et al. A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1982,4(2):157-166.

[12]Lopes A, Touzi R,Nezry E. Adaptive Speckle Filters and Scene Heterogeneity[J].IEEE Transaction on Geoscience and Remote Sensing,1990,28(6):992-1000.

[13]郭华东.雷达对地观测理论与应用[M]. 北京:科学出版社,2001.

[14]Oliver C,Quegan S. Understanding Synthetic Aperture Radar Images[M]. Boston and London:Artech House,1998.

[15]袁礼海,宋建社,薛文通,等.利用灰度和纹理特征的SAR图像分类研究[J]. 电光与控制,2007,14(4):58-62.

[16]宋建社,郑永安,袁礼海,等.合成孔径雷达图像理解与应用[M]. 北京:科学出版社,2008.

[17]Lain H,Woodhouse. Introduction to Microwave Remote Sensing[M].Taylor & Francis,2006.

[18]舒宁,马洪超,孙和利. 模式识别的理论和方法[M]. 武汉:武汉大学出版社,2004.

[1] ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[2] XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[3] SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[4] LIU Zhizhong, SONG Yingxu, YE Runqing. An analysis of rainstorm-induced landslides in northeast Chongqing on August 31, 2014 based on interpretation of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(4): 192-199.
[5] ZHANG Chengye, XING Jianghe, LI Jun, SANG Xiao. Recognition of the spatial scopes of tailing ponds based on U-Net and GF-6 images[J]. Remote Sensing for Natural Resources, 2021, 33(4): 252-257.
[6] LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[7] WANG Yiuzhu, HUANG Liang, CHEN Pengdi, LI Wenguo, YU Xiaona. Change detection of remote sensing images based on the fusion of co-saliency difference images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 89-96.
[8] LIU Wanjun, GAO Jiankang, QU Haicheng, JIANG Wentao. Ship detection based on multi-scale feature enhancement of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 97-106.
[9] SANG Xiao, ZHANG Chengye, LI Jun, ZHU Shoujie, XING Jianghe, WANG Jinyang, WANG Xingjuan, LI Jiayao, YANG Ying. Application of intensity analysis theory in the land use change in Yijin Holo Banner under the background of coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(3): 148-155.
[10] LU Qi, QIN Jun, YAO Xuedong, WU Yanlan, ZHU Haochen. Buildings extraction of GF-2 remote sensing image based on multi-layer perception network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 75-84.
[11] HU Suliyang, LI Hui, GU Yansheng, HUANG Xianyu, ZHANG Zhiqi, WANG Yingchun. An analysis of land use changes and driving forces of Dajiuhu wetland in Shennongjia based on high resolution remote sensing images: Constraints from the multi-source and long-term remote sensing information[J]. Remote Sensing for Land & Resources, 2021, 33(1): 221-230.
[12] LIU Zhao, ZHAO Tong, LIAO Feifan, LI Shuai, LI Haiyang. Research and comparative analysis on urban built-up area extraction methods from high-resolution remote sensing image based on semantic segmentation network[J]. Remote Sensing for Land & Resources, 2021, 33(1): 45-53.
[13] ZHENG Zhiteng, FAN Haisheng, WANG Jie, WU Yanlan, WANG Biao, HUANG Tengjie. An improved double-branch network method for intelligently extracting marine cage culture area[J]. Remote Sensing for Land & Resources, 2020, 32(4): 120-129.
[14] WANG Xiaobing. Denoising algorithm based on the fusion of lifting wavelet thresholding and multidirectional edge detection of remote sensing image of mining area[J]. Remote Sensing for Land & Resources, 2020, 32(4): 46-52.
[15] WEI Hongyu, ZHAO Yindi, DONG Jihong. Cooling tower detection based on the improved RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(4): 68-73.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech