28 October 2006 A novel image change detection method based on enhanced growing self-organization feature map
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Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 641915 (2006) https://doi.org/10.1117/12.713024
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China
Abstract
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.
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Yan Song, Xiuxiao Yuan, Honggen Xu, Yun Yang, "A novel image change detection method based on enhanced growing self-organization feature map", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 641915 (28 October 2006); doi: 10.1117/12.713024; https://doi.org/10.1117/12.713024
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