Target detection in hyperspectral Imaging using logistic regression

Edisanter Lo; Emmett Ientilucci

doi:10.1117/12.2223943

17 May 2016 Target detection in hyperspectral Imaging using logistic regression

Edisanter Lo, Emmett Ientilucci

Proceedings Volume 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII; 98400W (2016) https://doi.org/10.1117/12.2223943
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Target detection is an important application in hyperspectral imaging. Conventional algorithms for target detection assume that the pixels have a multivariate normal distribution. The pixels in most images do not have multivariate normal distributions. The logistic regression model, which does not require the assumption of multivariate normal distribution, is proposed in this paper as a target detection algorithm. Experimental results show that the logistic regression model can work well in target detection.

Citation Download Citation

Edisanter Lo and Emmett Ientilucci "Target detection in hyperspectral Imaging using logistic regression", Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98400W (17 May 2016); https://doi.org/10.1117/12.2223943

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