Advanced Search
JIN Chunbai, YANG Guang, LEI Yan, WU Di, LIU Wenjing. Study on vegetation camouflage exposure in Relief-F screening band[J]. LASER TECHNOLOGY, 2022, 46(1): 125-128. DOI: 10.7510/jgjs.issn.1001-3806.2022.01.013
Citation: JIN Chunbai, YANG Guang, LEI Yan, WU Di, LIU Wenjing. Study on vegetation camouflage exposure in Relief-F screening band[J]. LASER TECHNOLOGY, 2022, 46(1): 125-128. DOI: 10.7510/jgjs.issn.1001-3806.2022.01.013

Study on vegetation camouflage exposure in Relief-F screening band

More Information
  • Received Date: December 29, 2020
  • Revised Date: March 09, 2021
  • Published Date: January 24, 2022
  • In order to quickly expose vegetation camouflage and transform the hyperspectral research problem into the multi-spectral application problem, the hyperspectral band was selected based on the Relief-F algorithm was selected for the study of vegetation camouflage. First, the common plant spruce was used to simulate vegetation camouflage targets, and the HH2 ground-object spectrometer was used to collect experimental data. Then, the author introduced the Relief-F algorithm to screen the subset of feature bands, and conducted classification experiments with the subset of band obtained by other two common algorithms.The results show that the classification accuracy of using the Relief-F algorithm to choose the feature band subset is up to 96.4%, which is higher than the other two algorithms. This research is helpful for exposing the camouflage problem of vegetation.
  • [1]
    ZHANG B, GAO L R. Hyperspectral image classification and target detection[M]. Beijing: Science Press, 2011: 1-20(in Chinese).
    [2]
    YANG Zh J. Introduction to camouflage stealth technology for military targets[M]. Beijing: National Defense Industry Press, 2014: 15-31(in Chinese).
    [3]
    MA Y P, ZHANG W, LIU D X. Characteristics of hyperspectral reconnaissance and threat to ground military targets[J]. Aerospace Shanghai, 2012, 29(1): 37-41(in Chinese). http://www.cnki.com.cn/Article/CJFDTotal-SHHT201201009.htm
    [4]
    LIU Zh M, HU B R, WU W J. Spectral imaging of green coating camouflage under hyperspectral detection[J]. Acta Photonica Sinica, 2009, 38(4): 885-890(in Chinese).
    [5]
    WEI Zh T, HUANG J, FANG Q. Vegetation camouflage assessment based on analysis of characteristics of vegetative landscape[J]. Journal of PLA University of Science and Technology(Natural Science Edition), 2014, 15(1): 51-55(in Chinese).
    [6]
    HUGHES G. On the mean accuracy of statistical pattern recognizers[J]. IEEE Transactions on Information Theory, 1968, 14(1): 55-63. DOI: 10.1109/TIT.1968.1054102
    [7]
    ZHANG L, SHAO Zh F. Hyperspectral remote sensing image classification based on improved OIF and SVM algorithm[J]. Science of Surveying and Mapping, 2014, 39(11): 114-117(in Chinese).
    [8]
    WANG Q, YANG G, XIANG Y J. Band selection method of hyperspectral image based on subspace partition[J]. Ship Electronic Engineering, 2017, 37(4): 98-102(in Chinese). http://en.cnki.com.cn/Article_en/CJFDTOTAL-JCGC201704025.htm
    [9]
    WANG Q H, HUA W Sh, HUANG F Y. Hyperspectral anomaly detection algorithm based on spectral angle background purification[J]. Laser Technology, 2020, 44(5): 623-627(in Chinese).
    [10]
    YAN Y, HUA W Sh, ZHANG Y. An improved method of hyperspectral endmember extraction based on band selection[J]. Laser Technology, 2019, 43(4): 574-578(in Chinese). http://en.cnki.com.cn/Article_en/CJFDTotal-JGJS201904024.htm
    [11]
    CHEN Zh K, GUO R, CHENG P F. Application of LIF technology-based spectral feature extraction in oil detection[J]. Laser & Optoelectronics Progress, 2020, 57(13): 133002(in Chinese). http://www.researchgate.net/publication/343479939_Application_of_LIF_Technology-Based_Spectral_Feature_Extraction_in_Oil_Detection
    [12]
    HE Y, WANG J F. Rapid nondestructive identification of wood lacquer using raman spectroscopy basedon characteristic-band-fisher-k nearest neighbor[J]. Laser & Optoelectronics Progress, 2020, 57(1): 013001(in Chinese).
    [13]
    KIRA K, RENDELL L A. The feature selection problem: Traditional methods and a new algorithm[C]//Proceedings of 10th National Conference on Artificial Intelligence. New York, USA: IEEE, 1992: 129-134.
    [14]
    HUANG X J. Relief-based feature selection algorithms[D]. Suzhou: Soochow University, 2018: 81-92(in Chinese).
    [15]
    KONONENKO I. Estimating attributes: Analysis and extensions of Relief[C]//Machine Learning ECML-94. New York, USA: IEEE, 1994: 171-182.
  • Cited by

    Periodical cited type(2)

    1. 李静,王雅清,陆亚婷,周杰,倪晓昌. 皮秒激光在微结构制备中的应用. 轻工科技. 2024(02): 91-93 .
    2. 乔健,吴振铎,彭信翰,冉雨宣,杨景卫. Micro-LED芯片激光去除机理及工艺参数优化. 光学精密工程. 2024(09): 1360-1370 .

    Other cited types(1)

Catalog

    Article views (8) PDF downloads (6) Cited by(3)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return