High performance change detection in hyperspectral images using multiple references

Change detection normally involves one reference image and one test image. The objective is to detect changes that are not caused by illumination, atmospheric interferences, and mis-registration and parallax between the two images. Conventional methods can alleviate these issues to some extent. Since there may be some applications where there are multiple reference images collected over time, it would be ideal to incorporate multiple reference images to further improve the change detection performance. In this paper, we present a new approach to change detection, which can explicitly incorporate multiple reference images into account. Extensive experiments using actual hyperspectral images clearly demonstrated the performance of the new approach.

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