An Automatic SAR Image Segmentation Framework by Multi-objective Clustering and Artificial Immune Learning

DOI

10.2991/mmsa-18.2018.54How to use a DOI?

Keywords

evolutionary computation; artificial immune system; SAR image segmentation; multi-objective optimization; automatic clustering; watershed transformation

Abstract

Though several algorithms inspired by theoretical immunology have been applied to the domain of pattern classification, little focus has been placed on the issues that simultaneously optimize more than one objective-functions. Here, an efficient multi-objective automatic segmentation framework (MASF) is formulated and applied to SAR image unsupervised classification. In the framework, four important issues are presented: 1) two reasonable image preprocessing techniques are discussed at the initial stage; 2)then, an efficient immune multi-objective optimization algorithm is proposed; 3) besides, a locus-based adjacency representation in individual encoding is introduced; 4) two very simple, but very efficient conflicting clustering validity indices are incorporated into the framework and simultaneously optimized. Both simulated data and real images are used to quantitatively validate its effectiveness. In addition, four other state-of-the-art image segmentation methods are employed for comparison. Experimental results show that the proposed framework is efficient and effective for SAR image segmentation.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Dongdong Yang
AU  - Xiaowei Zhang
AU  - Lintao Lv
AU  - Wenzhun Huang
PY  - 2018/03
DA  - 2018/03
TI  - An Automatic SAR Image Segmentation Framework by Multi-objective Clustering and Artificial Immune Learning
BT  - Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)
PB  - Atlantis Press
SP  - 238
EP  - 243
SN  - 1951-6851
UR  - https://doi.org/10.2991/mmsa-18.2018.54
DO  - 10.2991/mmsa-18.2018.54
ID  - Yang2018/03
ER  -