IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i3p1902-d1040715.html
   My bibliography  Save this article

Infrared Small-Target Detection Using Multidirectional Local Difference Measure Weighted by Entropy

Author

Listed:
  • Huang Yao

    (Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China)

  • Liping Liu

    (Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China)

  • Yantao Wei

    (Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China)

  • Di Chen

    (Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China)

  • Mingwen Tong

    (Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China)

Abstract

Detecting small targets from infrared remote sensing images is still a challenging task. In this research, we propose a multidirectional local difference measure weighted by entropy (MDLDE) to detect small targets from infrared images with messy backgrounds. First, a new multidirectional local difference measure is proposed to suppress the clutter background. Then, the entropy, which captures the overall heterogeneity between the target and the background, is utilized to enhance the target. Lastly, an adaptive threshold was adopted to segment the target region from the background. The designed MDLDE could effectively enhance the target and simultaneously suppress the background clutter. Experimental results on six datasets indicate that the proposed method outperformed other state-of-the-art methods in terms of the signal-to-clutter ratio gain (SCRG), background suppression factor (BSF), and receiver operating characteristic (ROC) curves.

Suggested Citation

  • Huang Yao & Liping Liu & Yantao Wei & Di Chen & Mingwen Tong, 2023. "Infrared Small-Target Detection Using Multidirectional Local Difference Measure Weighted by Entropy," Sustainability, MDPI, vol. 15(3), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1902-:d:1040715
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/3/1902/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/3/1902/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1902-:d:1040715. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.