Please wait a minute...
REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (4) : 173-178     DOI: 10.6046/gtzyyg.2017.04.26
High-precise extraction for water on the Loess Plateau region from high resolution satellite image
SUN Na1, GAO Zhiqiang1,2, WANG Xiaojing1, LUO Zhidong3
1. Beijing Datum Technology Development Co. Ltd., Beijing 100084, China;
2. Beijing Forestry University, Beijing 100083, China;
3. Monitoring Center of Soil and Water Conservation, Ministry of Water Resources, Beijing 100053, China
Download: PDF(7258 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  In the Loess Plateau region, it is difficult to extract the complex water automatically and accurately in a large area, and hence a new water extraction method is proposed in this paper, which combines the object-based image analysis and seeded region growing algorithm. In the first step, it uses object-based image analysis to extract the main part of the water body according to the different water features and form the seeds region of water area. Then based on the result, the seeds grew to the precise shape of water. Extraction result shows that the method is effective, high precise and high efficient.
Keywords coal mine      subsidence disaster      remote sensing      dynamic monitoring     
:  TP79  
Issue Date: 04 December 2017
E-mail this article
E-mail Alert
Articles by authors
WANG Xiaohong
JING Qingqing
ZHOU Yingjie
YAO Weiling
Cite this article:   
WANG Xiaohong,JING Qingqing,ZHOU Yingjie, et al. High-precise extraction for water on the Loess Plateau region from high resolution satellite image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 173-178.
URL:     OR
[1] 杜云艳,周成虎.水体的遥感信息自动提取方法[J].遥感学报,1998,2(4):264-269.
Du Y Y,Zhou C H.Automatically extracting remote sensing information for water bodies[J].Journal of Remote Sensing,1998,2(4):264-269.
[2] McFeeters S K.The use of normalized difference water index(NDWI) in the delineation of open water features[J].International Journal of Remote Sensing,1996,17(7):1425-1432.
[3] 徐涵秋.利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J].遥感学报,2005,9(5):589-595.
Xu H Q.A study on information extraction of water body with the modified normalized difference water index(MNDWI)[J].Journal of Remote Sensing,2005,9(5):589-595.
[4] 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.
Du J K,Huang Y S,Feng X Z,et al.Study on water bodies extraction and classification from SPOT image[J].Journal of Remote Sensing,2001,5(3):214-219.
[5] 黄春龙,邢立新,韩 冬.基于纹理特征的水系信息提取[J].吉林大学学报(地球科学版),2008,38(S):226-228,250.
Huang C L,Xing L X,Han D.Extracting the information of water system based on texture features[J].Journal of Jilin University(Earth Science Edition),2008,38(S):226-228,250.
[6] 杨树文,薛重生,刘 涛,等.一种利用TM影像自动提取细小水体的方法[J].测绘学报,2010,39(6):611-617.
Yang S W,Xue C S,Liu T,et al.A method of small water information automatic extraction from TM remote sensing images[J].Acta Geodaetica et Cartographica Sinica,2010,39(6):611-617.
[7] 骆剑承,盛永伟,沈占锋,等.分步迭代的多光谱遥感水体信息高精度自动提取[J].遥感学报,2009,13(4):604-615.
Luo J C,Sheng Y W,Shen Z F,et al.Automatic and high-precise extraction for water information from multispectral images with the step-by-step iterative transformation mechanism[J].Journal of Remote Sensing,2009,13(4):604-615.
[8] 梅安新,彭望琭,秦其明,等.遥感导论[M].北京:高等教育出版社,2001.
Mei A X,Peng W L,Qin Q M,et al.An Introduction to Remote Sensing[M].Beijing:Higher Education Press,2001.
[9] 段 锐,管一弘.医学图像自动多阈值分割[J].计算机应用,2008,28(S2):196-197.
Duan R,Guan Y H.Multi-threshold value segmentation approach for medical images[J].Computer Applications,2008,28(S2):196-197.
[10] 毕海芸,王思远,曾江源,等.基于TM影像的几种常用水体提取方法的比较和分析[J].遥感信息,2012,27(5):77-82.
Bi H Y,Wang S Y,Zeng J Y,et al.Comparison and analysis of several common water extraction methods based on TM image[J].Remote Sensing Information,2012,27(5):77-82.
[11] Adams R,Bischof L.Seeded region growing[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,16(6):641-647.
[1] LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[2] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[3] LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[4] 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.
[5] 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.
[6] 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.
[7] LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[8] DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[9] GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[10] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[11] HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[12] AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[13] LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
[14] LIU Bailu, GUAN Lei. An improved method for thermal stress detection of coral bleaching in the South China Sea[J]. Remote Sensing for Natural Resources, 2021, 33(4): 136-142.
[15] WU Fang, JIN Dingjian, ZHANG Zonggui, JI Xinyang, LI Tianqi, GAO Yu. A preliminary study on land-sea integrated topographic surveying based on CZMIL bathymetric technique[J]. Remote Sensing for Natural Resources, 2021, 33(4): 173-180.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech