Classification of synthetic aperture radar images using Markov random field and textural features

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Synthetic aperture radar (SAR) is an active imagery system which allows day-and-night and all-weather acquisitions. SAR images are usually affected by a multiplicative noise depending on the ground reflectivity due to the coherence of the radar wavelength [1]. For this reason, classification of SAR images is not a straightforward task, and pixel based classification algorithms will struggle to achieve decent results. a possible solution to this problem is utilizing the spatial relationship between neighboring pixels. The Statistical dependency between neighboring pixels is modeled by Markov Random Field (MRF). In this paper, we present a novel classification algorithm for SAR images using MRF model. The method is based on an iterative expectation maximization (EM) procedure. The iterative process can be initialized by a texture grade images. In this way we avoid all manual intervention. In addition, we suggest an improvement for an existing classification algorithm [2] by using our EM procedure and texture images for expanding the MRF model to a 3-D model. The algorithm estimates the probability density function (PDF) of each class by a pre-defined, dictionary based, stochastic expectation maximization (SEM) procedure [3].

Original languageEnglish
Title of host publication2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781479959877
DOIs
StatePublished - 1 Jan 2014
Event2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014 - Eilat, Israel
Duration: 3 Dec 20145 Dec 2014

Publication series

Name2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014

Conference

Conference2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
Country/TerritoryIsrael
CityEilat
Period3/12/145/12/14

Keywords

  • MRF
  • SAR image classification
  • Textural features

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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