Hyperspectral data cube segmentation analysis in sub-pixel target detection

Eliya Ben Avraham; Stanley R. Rotman

doi:10.1117/12.2585067

12 April 2021 Hyperspectral data cube segmentation analysis in sub-pixel target detection

Proceedings Volume 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII; 1172711 (2021) https://doi.org/10.1117/12.2585067
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Segmentation is useful during sub-pixel target detection in hyperspectral data. Previous works have showed that improving performance in sub-pixel target detection can be achieved by making better estimates of the covariance matrix by using segmentation. One of the challenges mentioned was that pixel assignment has been influenced by the target, and therefore a reassignment after segmentation was needed. We examined and compared several methods to deal with this challenge before the segmentation process, as well as to check if this was essential for our algorithm’s success. Using simulations and several analytical tools we analyzed the matched-filter algorithm, both with and without segmentation, and compare performances of the receiver operating characteristic curves. We found there is no need to perform reassignment after segmentation; segmentation is effective even with the presence of the target in the examined pixel.

Conference Presentation

Citation Download Citation

Eliya Ben Avraham and Stanley R. Rotman "Hyperspectral data cube segmentation analysis in sub-pixel target detection", Proc. SPIE 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, 1172711 (12 April 2021); https://doi.org/10.1117/12.2585067

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