Parameter Optimization in Multi-scale Segmentation of High Resolution Remotely Sensed Image and Its Application in Object-oriented Classification

DOI

10.2991/eers-15.2015.21How to use a DOI?

Keywords

segmentation; parameter optimization; object-oriented classification; high resolution remotely sensed image

Abstract

With the widely use of high resolution remotely sensed image, the methodology of object-oriented classification emerged and became an active research area. Two basic steps of object-oriented classification method is segmentation and classification, and the effect of segmentation will directly affect the accuracy of classification. An optimal parameters method of multi-scale segmentation was described in this paper with the SPOT-5 high spatial resolution image of Pingtan Island in Fujian province, P. R. China. The cross validation technology was employed to compared the optimal value of the 4 parameters as scale, shape, color and compactness. The classification results of the traditional pixel-based supervised classification method and the object-oriented classification method were also checked using error matrix to qualify which method would be better. The result shows that object-oriented classification can make full use of spectral information, geometric structure and texture features of the image, and it has a better prospect in classification of the high spatial resolution remote sensing image.

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/).

Download article (PDF)

TY  - CONF
AU  - Lan Zheng
PY  - 2015/09
DA  - 2015/09
TI  - Parameter Optimization in Multi-scale Segmentation of High Resolution Remotely Sensed Image and Its Application in Object-oriented Classification
BT  - Proceedings of the 2015 International Conference on Environmental Engineering and Remote Sensing
PB  - Atlantis Press
SP  - 84
EP  - 90
SN  - 2352-538X
UR  - https://doi.org/10.2991/eers-15.2015.21
DO  - 10.2991/eers-15.2015.21
ID  - Zheng2015/09
ER  -