Unsupervised Multi-Class Change Detection in Bitemporal Multispectral Images Using Band Expansion

This paper focuses on solving the multi-class change detection problem in bitemporal multispectral remote sensing images. In that case, information that represented in a small number (e.g., two) of the original bands may be insufficient for the accurate identification of a few of multi-class changes. In particular, this problem becomes more difficult in unsupervised change detection cases when ground reference data is not available. In this paper, a solution is proposed by using the potential information represented in expanded features that constructed from the original spectral bands. Experimental results obtained on a real bitemporal remote sensing data set confirm the effectiveness of the proposed approach.