Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2008, Vol. 20 Issue (1) : 79-82     DOI: 10.6046/gtzyyg.2008.01.18
Technology Application |
THE OBJECT-ORIENTED METHOD FOR WETLAND INFORMATION EXTRACTION

SUN Yong-jun 1,2, TONG Qing-xi 1,3, QIN Qi-ming 1
1. Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China; 2. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China; 3. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China
Download: PDF(347 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

This paper deals with the utilization of the object-oriented method to extract wetland information from the Landsat ETM image. The test area is located in the source area of the Yellow River, and the wetland information extracted includes that of swamps and lakes. The object-oriented method consists of four steps, i.e., image segmentation, class hierarchy building, feature choice and classification, and precision evaluation. The test results show that the object-oriented method can effectively extract wetland information with smooth borders and avoid the pepper shape effect. With some adjustment the method can be widely applied to other areas. It can raise the automation degree of information extraction, reduce artificial workload and improve working efficiency.

Keywords Remote sensing images      Landuse      Greening covered     
: 

TP79

 
Issue Date: 13 July 2009
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Cite this article:   
SUN Yong-Jun, TONG Qing-Xi, QIN Qi-Ming. THE OBJECT-ORIENTED METHOD FOR WETLAND INFORMATION EXTRACTION[J]. REMOTE SENSING FOR LAND & RESOURCES,2008, 20(1): 79-82.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2008.01.18     OR     https://www.gtzyyg.com/EN/Y2008/V20/I1/79
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] Bai, Yuying, Chengling, Yanru, Shihu. Different remote sensing image matching methods based on multiple constraints[J]. Remote Sensing for Land & Resources, 2020, 32(3): 49-54.
[6] WU Tong, PENG Ling, HU Yuan. Informal garbage dumps detection in high resolution remote sensing images based on SU-RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(3): 90-97.
[7] Li XUE, Shuwen YANG, Jijing MA, Xin JIA, Ruliu YAN. Automatic expansion extraction algorithm of remote sensing images[J]. Remote Sensing for Land & Resources, 2019, 31(1): 42-48.
[8] Wei HUANG, Huixian HUANG, Jianmin XU, Jiating LIU. An improved road extraction method for remote sensing images based on Canny edge detection[J]. Remote Sensing for Land & Resources, 2019, 31(1): 65-70.
[9] Kang ZHANG, Baoqin HEI, Shengyang LI, Yuyang SHAO. Complex scene classification of remote sensing images based on CNN[J]. Remote Sensing for Land & Resources, 2018, 30(4): 49-55.
[10] Lijuan WANG, Xiao JIN, Hujun JIA, Yao TANG, Guochao MA. Change detection for mine environment based on domestic high resolution satellite images[J]. Remote Sensing for Land & Resources, 2018, 30(3): 151-158.
[11] Ye LYU, Xiangyun HU. Road extraction by incremental Markov random field segmentation from high spatial resolution remote sensing images[J]. Remote Sensing for Land & Resources, 2018, 30(3): 76-82.
[12] DENG Zeng, LI Dan, KE Yinghai, WU Yanchen, LI Xiaojuan, GONG Huili. An improved SVM algorithm for high spatial resolution remote sensing image classification[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 12-18.
[13] GAO Yongzhi, CHU Yu, LIANG Wei. Remote sensing monitoring and analysis of tailings ponds in the ore concentration area of Heilongjiang Province[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(1): 160-163.
[14] QUE Haoyi, HUANG Huixian, XU Jianmin. Road edge detection based on dual-threshold SSDA template matching[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(4): 29-33.
[15] SONG Zhili. Automatic approach based on point and curve features for multimodal remote sensing image registration[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(3): 48-54.
Viewed
Full text


Abstract

Cited

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