A machine learning approach for material detection in hyperspectral images

Author

Maree, Raphael ; Stevens, Brian ; Geurts, Pierre ; Guern, Yves ; Mack, Philippe

Author_Institution

GIGA, Univ. of Liege, Liege, Belgium

fYear

2009

fDate

20-25 June 2009

Firstpage

106

Lastpage

111

Abstract

In this paper we propose a machine learning approach for the detection of gaseous traces in thermal infra red hyperspectral images. It exploits both spectral and spatial information by extracting subcubes and by using extremely randomized trees with multiple outputs as a classifier. Promising results are shown on a dataset of more than 60 hypercubes.

Keywords

feature extraction; geophysical signal processing; image classification; learning (artificial intelligence); object detection; gaseous traces detection; image classification; machine learning approach; material detection; spatial information; spectral information; subcubes extraction; thermal infra red hyperspectral images; Classification tree analysis; Data mining; Hypercubes; Hyperspectral imaging; Image segmentation; Layout; Machine learning; Pixel; Testing; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on

Conference_Location

Miami, FL

ISSN

2160-7508

Print_ISBN

978-1-4244-3994-2

Type

conf

DOI

10.1109/CVPRW.2009.5204119

Filename

5204119

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2960430