Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2010, Vol. 22 Issue (3) : 114-119     DOI: 10.6046/gtzyyg.2010.03.23
Technology Application |
Research on Shallow Groundwater Information Extraction Based on Data Fusion
YU De-hao 1,2, LONG Fan 1, FANG Hong-bin 3, HAN Tian-cheng 1
1. Engineering Research Institute, Shenyang Military Area Command, Shenyang 110162, China; 2. 65056 Troops, Tieling 112000,China; 3. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China
Download: PDF(1142 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

 Aimed at improving the accuracy and efficiency of shallow groundwater exploration and opening up a new way to seek groundwater by remote sensing, this paper presents a new fusion algorithm based on Principal Component Analysis (PCA) and Wavelet Transformation (WT) by using Landsat-7 ETM data (spatial resolution being 30 m) and Envisat-1 ASAR data (Wide Swath Mode, spatial resolution being 150 m) as the main fusion data. According to the new fusion algorithm, anomaly information of shallow groundwater was successfully extracted. In combination with field investigation, geophysical exploration and drilling, the forecasting results of rating I, II and III were in accordance with the actual state, and rich shallow groundwater was found. It is thus concluded that the method has some feasibility and practicability, and can serve as a new technique for rapid exploration of groundwater in the future.

Keywords RS      GIS      Groundwater resource     
: 

 

 
  TP 79

 
Issue Date: 20 September 2010
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
WU Quan-yuan
HOU Wei
AN Guo-qiang
Cite this article:   
WU Quan-yuan,HOU Wei,AN Guo-qiang. Research on Shallow Groundwater Information Extraction Based on Data Fusion[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(3): 114-119.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.03.23     OR     https://www.gtzyyg.com/EN/Y2010/V22/I3/114

[1]Daily M I,Farr T,Elachi C. Geologic Interpretation from Composited Radar and Landsat Imagery [J]. Photogram Eng Remote Sensing, 1979,45(8):1109-1116.

[2]Ray P K,Roy A K,Prabhakaran B. Evaluation and Integration of ERS-1 ASAR and Optical Sensor Data (TM and IRS) for Geological Investigations Photon Irvachak [J]. Journal of Indian Society of Remote Sensing,1995,23:77-86.

[3]Demargne L,Nezry E,Yakam S F,et al. Use of SPOT and Radar Data for Forestry Inventory in Sarawak, Malaysia [C]// Proceedings of the Conference on Data Management and Modeling Using Remote Sensing and GIS for Tropical Forest Land Inventory. Jakarta,1998:111-119.

[4]王峰,荆燕, 陈志军. 运用Radarsat与ETM数据融合探测隐伏断裂[J]. 地震研究, 2004, 27(4):369-373.

[5]郭广猛,曹云刚,马龙. ENVISAT-ASAR数据处理介绍[J]. 遥感信息,2006(4):61-63.

[6]余钧辉,张万昌,乐通潮. 基于小波变换的MODIS与ETM数据融合研究[J]. 遥感信息,2004 (4):39-43.

[7]Reed J M,Hutchinson S. Image Fusion and Subpixel Parameter Estimation for Automated Optical Inspection of Electronic Components [J]. Industrial Electronics,1996,43(3):346-354.

[8]Richards J A. Thematic Mapping from Multitemporal Image Data Using the Principal Component Transformation [J]. Remote Sensing of Environment,1984,16:36-46.

[9]Therrien C W,Scrofani J W,Krebs W K. An Adaptive Technique for the Enhanced Fusion of Low Light Visible with Uncooled Thermal Infrared Imagery [C]//International Conference on Imaging Processing.Washington,1997:405-412.

[10]Toet A,Ijspeert J K,Wanan A M. Fusion of Visible and Thermal Imagery Improves Situational Awareness [J]. Displays,1997,18(2):85-95.

[11]Sharma R K,Ixen T K,Pavel M. Probabilistic Image Sensor Fusion [J]. Advances in Neural Information Processing Systems,1999(11):203-207.

[12]Wright W A,Bristol F. Quick Markov Random Field Image Fusion [C]// Proceedings of the Conference on Signal Processing, Sensor Fusion and Target Recognition VII. Orlando, 1998:302-308.

[13]Broussard R P,Roges S K, Oxley M E,et al. Physiologically Motivated Image Fusion for Object Detection Using a Pulse Coupled Neural Network [J]. Neural Networks,1999 (3):554-563.

[14]Richard S,Sims F,Phillips M A. Target Signature Consistency of Image Data Fusion Alternatives [J].Optical Engineering,1997, 36(3):743-754.

[15]Leung L W,King B,Vohora V. Comparison of Image Data Fusion Techniques Using Entropy and INI [C]// The 22nd Asian Conference on Remote Sensing.Beijing,2001:203-210.

[1] SUN Yiming, ZHANG Baogang, WU Qizhong, LIU Aobo, GAO Chao, NIU Jing, HE Ping. Application of domestic low-cost micro-satellite images in urban bare land identification[J]. Remote Sensing for Natural Resources, 2022, 34(1): 189-197.
[2] SONG Qi, FENG Chunhui, MA Ziqiang, WANG Nan, JI Wenjun, PENG Jie. Simulation of land use change in oasis of arid areas based on Landsat images from 1990 to 2019[J]. Remote Sensing for Natural Resources, 2022, 34(1): 198-209.
[3] 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.
[4] WU Yijie, KONG Xuesong. Simulation and development mode suggestions of the spatial pattern of “ecology-agriculture-construction” land in Jiangsu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 238-248.
[5] YAO Jinxi, ZHANG Zhi, ZHANG Kun. An analysis of the characteristics, causes, and trends of spatio-temporal changes in vegetation in the Nuomuhong alluvial fan based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2022, 34(1): 249-256.
[6] 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.
[7] LI Dong, TANG Cheng, ZOU Tao, HOU Xiyong. Detection and assessment of the physical state of offshore artificial reefs[J]. Remote Sensing for Natural Resources, 2022, 34(1): 27-33.
[8] 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.
[9] QU Haicheng, WAND Yaxuan, SHEN Lei. Hyperspectral super-resolution combining multi-receptive field features with spectral-spatial attention[J]. Remote Sensing for Natural Resources, 2022, 34(1): 43-52.
[10] ZANG Liri, YANG Shuwen, SHEN Shunfa, XUE Qing, QIN Xiaowei. A registration algorithm of images with special textures coupling a watershed with mathematical morphology[J]. Remote Sensing for Natural Resources, 2022, 34(1): 76-84.
[11] REN Chaofeng, PU Yuchi, ZHANG Fuqiang. A method for extracting match pairs of UAV images considering geospatial information[J]. Remote Sensing for Natural Resources, 2022, 34(1): 85-92.
[12] 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.
[13] CHEN Jie, ZHANG Lifu, ZHANG Linshan, ZHANG Hongming, TONG Qingxi. Research progress on online monitoring technologies of water quality parameters based on ultraviolet-visible spectra[J]. Remote Sensing for Natural Resources, 2021, 33(4): 1-9.
[14] GUO Xiaozheng, YAO Yunjun, JIA Kun, ZHANG Xiaotong, ZHAO Xiang. Information extraction of Mars dunes based on U-Net[J]. Remote Sensing for Natural Resources, 2021, 33(4): 130-135.
[15] GAO Wenlong, ZHANG Shengwei, LIN Xi, LUO Meng, REN Zhaoyi. The remote sensing-based estimation and spatial-temporal dynamic analysis of SOM in coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(4): 235-242.
Viewed
Full text


Abstract

Cited

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