SAR Image Segmentation Based on Maximum Variance Method and Morphology

Author:

Xiao Ming Xia1

Affiliation:

1. Beifang University of Nationalities

Abstract

A new technique that combines maximum variance method and morphology was presented for Synthetic Aperture Radar (SAR) image segmentation in target detection. Firstly, using the first-order differential method to enhance the original image for highlighting edge details of the image; then using the maximum variance method to calculate the gray threshold and segment the image; lastly, the mathematical morphology was used to processing the segmented image, which could prominently improve the segmentation effects. Experiments show that this algorithm can obtain accurate segmentation results, and have a good effect on noise suppression, edge detail protection and operation time.

Publisher

Trans Tech Publications, Ltd.

Subject

General Engineering

Reference14 articles.

1. Zhu jun, Wangshi xi: Improved 2D Otsu Algorithm for SAR Image[J], Journal of Image and Graphics, 2009. 01, 14-18.

2. Zhang hong, Wang chao. Zhang bo, Wu fan, Yan dongmei: High resolution of SAR image Target recognition. Beijing: Science press, (2009).

3. Wu junzheng, Yan wei dong, Bianhui: Target Segmentation for SAR Images Based on Nonsubsampled Contourlet Characteristic and PCNN. Opto-Electronic Engineering, 2012(39) 86-92.

4. Guillaume Delyon, philippe Refregier: SAR image segmentation by stochastic complexity minimization with a nonparametric noise model. IEEE transacyions on geosciences and remote sensing, VOL, 44, NO, JULY (2006).

5. Martins, C.I. O: Combining watershed and statistical analysis for SAR Image Segmentation. Proceedings of IEEE, (2006).

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