Detection Of Greenhouse Gases Using Infrared Hyperspectral Imagery

被引:0
|
作者
Gur, Yusuf [1 ]
Omruuzun, Fatih [1 ]
Ozisik Baskurt, Didem [1 ]
Yardimci Cetin, Yasemin [1 ]
机构
[1] Orta Dogu Tekn Univ, Bilisim Sistemleri Bolumu, Ankara, Turkey
关键词
infrared hyperspectral imaging; remote sensing; gas detection;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
One of the global issues that threatens human and environmental health is the harmful substances leaking to the atmosphere as a result of industrial, agricultural and other activities. These substances intensively contain gaseous, such as; water vapor, carbon dioxide, methane, nitrogen oxide and ozone that cause greenhouse effect. Due to the high-resolution information provided in both spatial and spectral domains, hyperspectral imagers have recently been used as an alternative method for standoff detection of these substances. The methods proposed in the literature use atmospheric models or estimation methods to obtain crucial parameters that are required for modelling the measured radiance. In this study, we propose a method to detect and identify greenhouse gaseous, which are released from different sources, that does not require the such parameters for modelling the measured radiance.
引用
收藏
页数:5
相关论文
共 50 条
  • [41] Anomaly detection in hyperspectral imagery: an overview
    Ben Salem, Manel
    Ettabaa, Karim Saheb
    Hamdi, Mohamed Ali
    2014 FIRST INTERNATIONAL IMAGE PROCESSING, APPLICATIONS AND SYSTEMS CONFERENCE (IPAS), 2014,
  • [42] Partially Supervised Detection in Hyperspectral Imagery
    Heinz, Daniel C.
    Bahr, Thomas
    Streutker, David
    Terrie, Greg
    Ingram, Michael
    ALGORITHMS, TECHNOLOGIES, AND APPLICATIONS FOR MULTISPECTRAL AND HYPERSPECTRAL IMAGING XXVIII, 2022, 12094
  • [43] Anomaly detection in noisy hyperspectral imagery
    Riley, RA
    Newsom, RK
    Andrews, AK
    IMAGING SPECTROMETRY X, 2004, 5546 : 159 - 170
  • [44] Characterization of anomaly detection in hyperspectral imagery
    Chang, Chein-I
    Hsueh, Mingkai
    Sensor Review, 2006, 26 (02) : 137 - 146
  • [45] Algorithms of target detection on hyperspectral imagery
    Yan, Yahui
    Liu, Bingqi
    OPTIK, 2013, 124 (23): : 6341 - 6344
  • [46] SALIENT OBJECT DETECTION IN HYPERSPECTRAL IMAGERY
    Liang, Jie
    Zhou, Jun
    Bai, Xiao
    Qian, Yuntao
    2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013), 2013, : 2393 - 2397
  • [47] Shadowed object detection for hyperspectral imagery
    Mayer, R.
    Antoniades, J.
    Baumback, M.
    Chester, D.
    Edwards, J.
    Goldstein, A.
    Haas, D.
    Henderson, S.
    INFRARED SPACEBORNE REMOTE SENSING AND INSTRUMENTATION XV, 2007, 6678
  • [48] DETECTION OF UNDERWATER OBJECTS IN HYPERSPECTRAL IMAGERY
    Gillis, David B.
    2016 8TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS), 2016,
  • [49] Anomaly detection and classification for hyperspectral imagery
    Chang, CI
    Chiang, SS
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2002, 40 (06): : 1314 - 1325
  • [50] Automatic target detection using dualband infrared imagery
    Chan, LCA
    Der, S
    Nasrabadi, NM
    2000 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, PROCEEDINGS, VOLS I-VI, 2000, : 2286 - 2289