DETECTION OF OIL SPILL BY COMPARING MARKOV RANDOM FIELD, ADAPTIVE THRESHOLDING & MORPHOLOGICAL CLOSING TECHNIQUES

Authors

  • Mukta Jagdish Research Scholar, Department of Computer Science and Engineering, School of Engineering, Vels Institute of Science Technology &Advanced Studies (VISTAS), Vels University, Chennai, TN, India
  • Jerritta.S Research Supervisor, Associate Professor (Electronics and Communication Engineering), School of Engineering, Vels Institute of Science Technology &Advanced Studies (VISTAS), Vels University, Chennai, TN, India

Keywords:

Oil spill, Gulf of Mexico, tracking, ASAR RADARSAT-2

Abstract

This research perform detection of oil spill by comparing Markov random field, adaptive thresholding &
morphological closing techniques which results in less time consuming, oil spill tracking, oil spill area, dark patches and
spill patterns. This work helps to regular monitoring of oil in the ocean. This approach helps to find out which algorithm is
best suited for detection of oil spill and tracking. This work is carried out using ASAR RADARSAT-2 image. In this research
three images was captured and monitored for oil spill detection. These images belong to different days, angles and
polarization. By comparing these algorithms it results morphological closing techniques is best suited for analysing oil spill
in ASAR images.

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Published

2018-02-25

How to Cite

Mukta Jagdish, & Jerritta.S. (2018). DETECTION OF OIL SPILL BY COMPARING MARKOV RANDOM FIELD, ADAPTIVE THRESHOLDING & MORPHOLOGICAL CLOSING TECHNIQUES. International Journal of Advance Engineering and Research Development (IJAERD), 5(2), 807–812. Retrieved from https://ijaerd.org/index.php/IJAERD/article/view/2497

Issue

Vol. 5 No. 2 (2018): Volume 5 Issue 2 February 2018

Section

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