High Resolution Remote Sensing Image Classification based on SVM and FCM
Qin Li, Wenxing Bao, Xing Li, Bin Li
Available Online June 2015.
- https://doi.org/10.2991/icecee-15.2015.236How to use a DOI?
- ALOS image; SVM; FCM; textural features
- 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.
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 -