Data summarization for hyperspectral image analysis

This paper explores data summarization methods to find reduced size representations of hyperspectral data. Operating on the small size summary reduces computational requirement further up in the image processing chain. We look into how to construct such summarizations and their application in endmember extraction for unmixing. We compare methods based on random sampling and methods based on superpixel representations. We show that summaries based on superpixel analysis better summarize the characteristics of the image compared to random sampling. Results with real images are presented.