High Resolution Remote Sensing Image Classification based on SVM and FCM

DOI

10.2991/icecee-15.2015.236How to use a DOI?

Keywords

ALOS image; SVM; FCM; textural features

Abstract

This paper proposes a remote sensing image classification method based on multi-feature combination of the support vector machine (SVM) according to the classification problems of the high resolution remote sensing image. ALOS image is operated at two stages by this method. The first stage is to coarsely classify with fuzzy c-means (FCM) algorithm and k-means algorithm, and the second stage is to extract the textural features of the image with gray-level co-occurrence matrix (GLCM). The relevancy is selected to participate in the classification of the SVM. Experiments prove that the method is an effective and feasible remote sensing image classification method.

Copyright

© 2015, 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/).

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TY  - CONF
AU  - Qin Li
AU  - Wenxing Bao
AU  - Xing Li
AU  - Bin Li
PY  - 2015/06
DA  - 2015/06
TI  - High Resolution Remote Sensing Image Classification based on SVM and FCM
BT  - Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics
PB  - Atlantis Press
SP  - 1271
EP  - 1278
SN  - 2352-538X
UR  - https://doi.org/10.2991/icecee-15.2015.236
DO  - 10.2991/icecee-15.2015.236
ID  - Li2015/06
ER  -