Data summarization for hyperspectral image analysis

Maher Aldeghlawi; Mohammed Q. Alkhatib; Miguel Velez-Reyes

doi:10.1117/12.2590762

14 April 2021 Data summarization for hyperspectral image analysis

Maher Aldeghlawi, Mohammed Q. Alkhatib, Miguel Velez-Reyes

Proceedings Volume 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII; 117271K (2021) https://doi.org/10.1117/12.2590762
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Conference Poster

Abstract

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.

Conference Presentation

Citation Download Citation

Maher Aldeghlawi, Mohammed Q. Alkhatib, and Miguel Velez-Reyes "Data summarization for hyperspectral image analysis", Proc. SPIE 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, 117271K (14 April 2021); https://doi.org/10.1117/12.2590762

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