Identification and filtering of rainfall and ground radar echoes using textural features

被引:12
|
作者
Haddad, B
Adane, A
Sauvageot, H
Sadouki, L
Naili, R
机构
[1] Univ Sci & Technol Algiers USTHB, Fac Elect & Comp Sci, Lab Image Proc & Radiat, Algiers, Algeria
[2] Univ Toulouse 3, Observ Midi Pyrenees, Lab Aerol, F-31400 Toulouse, France
[3] Off Natl Meteorol, Dar El Beida, Alger, Algeria
关键词
D O I
10.1080/01431160310001654455
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
This study deals with the identification of precipitation and ground echoes in radar images using their textural features. The images were collected by meteorological radars in the regions of Setif (Algeria) and Bordeaux (France). Two kinds of texture-based techniques have been considered, consisting in calculating either the histograms of grey levels or the histograms of their sum and difference. Hence, the first-order probability distributions were found to be sufficient to account for the textural features of radar images. Energy is found to be the textural parameter that clearly distinguishes between precipitation and ground echoes. With both methods, fixed ground echoes and anaprops are efficiently rejected, whereas precipitation echoes are kept almost unchanged. These methods have the advantages of effectiveness and simplicity. The threshold of discrimination is independent of the geographical and climatic conditions in the regions under study. Because the computation time needed by these techniques is small, the radar images can be processed in real-time.
引用
收藏
页码:4641 / 4656
页数:16
相关论文
共 50 条
  • [31] Ischemic Stroke Identification by Using Watershed Segmentation and Textural and Statistical Features
    Ajam, Mohammed
    Kanaan, Hussein
    el Khansa, Lina
    Ayache, Mohammad
    [J]. 2019 INTERNATIONAL ARAB CONFERENCE ON INFORMATION TECHNOLOGY (ACIT), 2019, : 255 - 258
  • [32] Identification of malware families using stacking of textural features and machine learning
    Kumar, Sanjeev
    Janet, B.
    Neelakantan, Subramanian
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2022, 208
  • [33] Correcting of real-time radar rainfall bias using a Kalman filtering approach
    Chumchean, S
    Seed, A
    Sharma, A
    [J]. JOURNAL OF HYDROLOGY, 2006, 317 (1-2) : 123 - 137
  • [34] Classification of Synthetic Aperture Radar images using Markov Random Field and textural features
    Benou, Ariel
    Rotman, Stanley R.
    Blumberg, Dan G.
    [J]. 2014 IEEE 28TH CONVENTION OF ELECTRICAL & ELECTRONICS ENGINEERS IN ISRAEL (IEEEI), 2014,
  • [35] Intelligent Parameters Identification of Radar Echoes in Yunnan Province
    Zhao, Na
    Wang, Jian
    Li, Peng
    Jiang, Zuo
    Xie, Zhongwen
    Mo, Qi
    [J]. ADVANCED RESEARCH ON MATERIAL SCIENCE, ENVIROMENT SCIENCE AND COMPUTER SCIENCE III, 2014, 886 : 568 - +
  • [36] Ground clutter filtering for Weather radar using staggered pulse repetition time
    Collado Rosell, A.
    Areta, J.
    Pascual, J. P.
    [J]. 2018 IEEE BIENNIAL CONGRESS OF ARGENTINA (ARGENCON), 2018,
  • [37] Particle Filtering Based Approach for Landmine Detection Using Ground Penetrating Radar
    Ng, William
    Chan, Thomas C. T.
    So, H. C.
    Ho, K. C.
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2008, 46 (11): : 3739 - 3755
  • [38] Revealing stratigraphy in ground-penetrating radar data using domain filtering
    Young, RA
    Sun, JS
    [J]. GEOPHYSICS, 1999, 64 (02) : 435 - 442
  • [39] Weather Radar Ground Clutter. Part II: Real-Time Identification and Filtering
    Hubbert, J. C.
    Dixon, M.
    Ellis, S. M.
    [J]. JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY, 2009, 26 (07) : 1181 - 1197
  • [40] Evaluation of Filtering Technique for Human Activity Identification using MIMO Radar
    Sasakawa, Dai
    Honma, Naoki
    Nakayama, Takeshi
    Iizuka, Shoichi
    [J]. 2017 INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION (ISAP 2017), 2017,