• DocumentCode
    2425907
  • Title

    Number estimation of small-sized ships in remote sensing image based on cumulative projection curve

  • Author

    Hu, Yonggang ; Wu, Yi

  • Author_Institution
    Dept. of Math. & Syst. Sci., Nat. Univ. of Defense Technol., Changsha
  • fYear
    2008
  • fDate
    7-9 July 2008
  • Firstpage
    1522
  • Lastpage
    1526
  • Abstract
    Ship detection is an important stage for the sea- area surveillance and many algorithms have been proposed for dealing with such tasks. Nevertheless, most of them are designed for large-sized ships and are not efficient for the small ones. In this paper, we present a novel method based on cumulative projection curve(CPC) to estimate the number of ships of small size. We firstly compute the Mahalanobis distance between each pixel of the image and the pixel intensities distribution of water, and then project these Mahalanobis distances to their near coastline vertically. The projected one-dimension curve is called cumulative projection curve. By doing this, each ship along the coastline will incur a fluctuating, the ship response, in the CPC. Thus, the number of ships can be estimated through the estimation for the number of ship responses in the CPC. This method simplifies the detection problem by converting a two-dimension problem to an one-dimension problem, and its efficiency is illustrated by the experimental results in the paper.
  • Keywords
    geophysical signal processing; object detection; remote sensing; CPC; Mahalanobis distance; cumulative projection curve; number estimation; remote sensing image; sea-area surveillance; ship detection; small-sized ships; Image segmentation; Marine vehicles; Mathematics; Optical sensors; Pixel; Remote monitoring; Remote sensing; Shape; Surveillance; Target recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Audio, Language and Image Processing, 2008. ICALIP 2008. International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-1723-0
  • Electronic_ISBN
    978-1-4244-1724-7
  • Type

    conf

  • DOI
    10.1109/ICALIP.2008.4590172
  • Filename
    4590172