Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics

High Resolution Remote Sensing Image Classification based on SVM and FCM

Authors
Qin Li, Wenxing Bao, Xing Li, Bin Li
Corresponding Author
Qin Li
Available Online June 2015.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2015 2nd International Conference on Electrical, Computer Engineering and Electronics
Part of series
Advances in Computer Science Research
Publication Date
June 2015
ISBN
978-94-62520-81-3
ISSN
2352-538X
DOI
https://doi.org/10.2991/icecee-15.2015.236How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 2015 2nd International Conference on Electrical, Computer Engineering and Electronics
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icecee-15.2015.236
DO  - https://doi.org/10.2991/icecee-15.2015.236
ID  - Li2015/06
ER  -