Skip to Main content Skip to Navigation
Conference papers

Performance Analysis of Robust Detectors for Hyperspectral Imaging

Abstract : When accounting for heterogeneity and non-Gaussianity of real hyperspectral data, elliptical distributions provide reliable models for background characterization. Through these assumptions, this paper highlights the fact that robust estimation procedures are an interesting alternative to classical methods and can bring some great improvement to the detection process. The goal of this paper is then not only to recall well-known methodologies of target detection but also to propose ways to extend them for taking into account the heterogeneity and non-Gaussianity of the hyperspectral images.
Complete list of metadata

https://hal-supelec.archives-ouvertes.fr/hal-00934285
Contributor : Anne-Hélène Picot <>
Submitted on : Friday, June 4, 2021 - 8:54:42 PM
Last modification on : Tuesday, June 15, 2021 - 4:18:44 PM

File

JFP2013.pdf
Files produced by the author(s)

Identifiers

Citation

Joana Frontera-Pons, Jean-Philippe Ovarlez, Frédéric Pascal, Jocelyn Chanussot. Performance Analysis of Robust Detectors for Hyperspectral Imaging. IGARSS 2013 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2013, Melbourne, Australia. pp.1-4, ⟨10.1109/IGARSS.2013.6721348⟩. ⟨hal-00934285⟩

Share

Metrics

Record views

555

Files downloads

19