Segmentation of high resolution remote sensing images by combining hidden Markov random field model and fuzzy c-means at the region level

DOI

10.2991/iceeecs-16.2016.242How to use a DOI?

Keywords

Image segmentation; Remote Sensing; Markov random field model; Fuzzy c-means; high resolution.

Abstract

In high spatial resolution remote-sensing images, complex landscapes are usually accompanied with macro texture patterns, which often adversely affect segmentation accuracy, mainly due to their high spatial and spectral heterogeneity. To address this problem, this study develops an image segmentation method by combining the iteration procedure of fuzzy c-means (FCM) clustering and hidden Markov random field (HMRF) model at the region level. The performance of the proposed method was assessed through aerial images. Results indicate that the proposed method can improve image segmentation accuracy, compared to FLICM, HMRF-FCM, MRR-MRF, and IRGS.

Copyright

© 2016, 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  - Xu Song
AU  - Guoying Liu
PY  - 2016/12
DA  - 2016/12
TI  - Segmentation of high resolution remote sensing images by combining hidden Markov random field model and fuzzy c-means at the region level
BT  - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
PB  - Atlantis Press
SP  - 1243
EP  - 1247
SN  - 2352-538X
UR  - https://doi.org/10.2991/iceeecs-16.2016.242
DO  - 10.2991/iceeecs-16.2016.242
ID  - Song2016/12
ER  -