Effective segmentation for point target detection

Yoram Furth; Stanley R. Rotman

doi:10.1117/12.2655794

13 June 2023 Effective segmentation for point target detection

Yoram Furth, Stanley R. Rotman

Proceedings Volume 12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX ; 125190O (2023) https://doi.org/10.1117/12.2655794
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Point target detection algorithms in hyperspectral imaging commonly use the spectral inverse covariance matrix to whiten the natural noise of the image. Since the noise in hyperspectral data cubes often suffer from a lack of stationarity, segmentation appears to be an attractive preprocessing operation. However, the literature contains examples of successful and unsuccessful segmentation with no plausible explanation for why some succeed, and others do not. Focusing on one representative algorithm and assuming a target additive model, this paper tracks the underlying causes of when segmentation does improve detection for different target spectra. It then characterizes a real dataset and concludes with ways to improve the detector performance.

Conference Presentation

Citation Download Citation

Yoram Furth and Stanley R. Rotman "Effective segmentation for point target detection", Proc. SPIE 12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX , 125190O (13 June 2023); https://doi.org/10.1117/12.2655794

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