Title :
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
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;
Conference_Titel :
Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4244-3994-2
DOI :
10.1109/CVPRW.2009.5204119
