Post-classification analysis is an important way for remotely sensed imagery change detection. In this paper, we propose a novel classification way for change detection using multispectral IKONOS imagery. The classification way is called after Enhanced Growing Self-Organization Map (EGSOM). The EGSOM is designed to solve two limitation of traditional Self Organization Feature Map (SOM). One is the training time of SOM is endless, the other is SOM's structure is fixed before train. EGSOM make use of Growing Self Organization Feature Map and the network's weights are initialized after hierachical clustering method. The method can save network-training time and make the network express input data correctly. Using EGSOM, we classify Multispectral IKONOS imagery and analyze the change detection result. The experiment shows the EGSOM can achieve better classification results than max likelihood method.

Date Published: 28 October 2006
PDF: 7 pages
Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 641915 (28 October 2006); doi: 10.1117/12.713024

Published in SPIE Proceedings Vol. 6419:
Geoinformatics 2006: Remotely Sensed Data and Information
Liangpei Zhang; Xiaoling Chen, Editor(s)