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A target-based color space for sea target detection

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Abstract

Sea target detection is a vital application for military and navigation purposes. A new supervised clustering method based on the combination of the PSO and FCM techniques is presented for the sea target detection problem. The color components of the target and non-target pixels in the RGB color space are used as features to train the classification algorithm. The new classifier is presented in the form of a new color space which we call the Target-based Color Space (TCS); in fact the RGB color space is converted to this new space through a 3×3 matrix. The Particle Swarm Optimization (PSO) algorithm is then used to search for the optimum weights of the conversion matrix which results in a more discriminating clustering space between the target and non-target pixels. In other words, solving the optimization problem, minimization of the objective function of the FCM clustering technique in linear and quadratic transform domain (with a NP-hard problem in quadratic conversion), is done using the PSO algorithm. The main objective of this work is to demonstrate the efficiency of using just color features, as well as color space conversion in the classification domain. Experimental results show the efficiency of new method in finding sea targets in color images.

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Author information

Authors and Affiliations

  1. Islamic Azad University Parand Branch, Parand, Iran

    Saeed Mirghasemi

  2. Ferdowsi University of Mashhad, Mashhad, Iran

    Hadi Sadoghi Yazdi

  3. Tarbiat Modares University of Tehran, Tehran, Iran

    Mojtaba Lotfizad

Authors
  1. Saeed Mirghasemi
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  2. Hadi Sadoghi Yazdi
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  3. Mojtaba Lotfizad
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Correspondence to Hadi Sadoghi Yazdi.

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Mirghasemi, S., Sadoghi Yazdi, H. & Lotfizad, M. A target-based color space for sea target detection. Appl Intell 36, 960–978 (2012). https://doi.org/10.1007/s10489-011-0307-y

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