An adaptive robust regression method: Application to galaxy spectrum baseline estimation

Raphael Bacher 1, 2 Florent Chatelain 3 Olivier Michel 1
1 GIPSA-CICS - CICS
GIPSA-DIS - Département Images et Signal
3 GIPSA-SAIGA - SAIGA
GIPSA-DA - Département Automatique, GIPSA-DIS - Département Images et Signal
Abstract : In this paper, a new robust regression method based on the Least Trimmed Squares (LTS) is proposed. The novelty of this approach consists in a simple adaptive estimation of the number of outliers. This method can be applied to baseline estimation, for example to improve the detection of gas spectral signature in astronomical hy-perspectral data such as those produced by the new Multi Unit Spec-troscopic Explorer (MUSE) instrument. To do so a method following the general idea of the LOWESS algorithm, a classical robust smoothing method, is developed. It consists in a windowed local linear regression, the local regression being done here by the new adap-tive LTS approach. The developed method is compared with state-of-the art baseline estimated algorithms on simulated data closed to the real data produced by the MUSE instrument.
Document type :
Conference papers
Liste complète des métadonnées

Cited literature [12 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01462974
Contributor : Raphael Bacher <>
Submitted on : Thursday, February 9, 2017 - 11:46:49 AM
Last modification on : Friday, April 5, 2019 - 8:04:59 PM
Document(s) archivé(s) le : Wednesday, May 10, 2017 - 1:05:03 PM

File

ArticleICASSP.pdf
Files produced by the author(s)

Identifiers

Citation

Raphael Bacher, Florent Chatelain, Olivier Michel. An adaptive robust regression method: Application to galaxy spectrum baseline estimation. 41st IEEE International Conference on Acoustics, Speech and SIgnal Processing (ICASSP 2016), Mar 2016, Shanghai, China. pp.4423 - 4427, ⟨10.1109/ICASSP.2016.7472513⟩. ⟨hal-01462974⟩

Share

Metrics

Record views

453

Files downloads

177