Object recognition in urban hyperspectral images using Binary Partition Tree representation

Author

Valero, S. ; Salembier, Philippe ; Chanussot, Jocelyn

Author_Institution

CNES, Univ. de Toulouse, Toulouse, France

fYear

2013

fDate

21-26 July 2013

Firstpage

4098

Lastpage

4101

Abstract

In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. The BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region-based perspective. Experimental results demonstrate the good performances of this BPT-based approach.

Keywords

geophysical image processing; hyperspectral imaging; image representation; object recognition; remote sensing; tree data structures; BPT representation; BPT-based approach; binary partition tree representation; image representation; object detection; object recognition; region-based perspective; remote sensing; robust object identification scheme; search space; spatial information; spectral information; urban hyperspectral images; Feature extraction; Hyperspectral imaging; Image segmentation; Merging; Object detection; Support vector machines; BPT; Object detection; Region-based image analysis; hyperspectral;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International

Conference_Location

Melbourne, VIC

ISSN

2153-6996

Print_ISBN

978-1-4799-1114-1

Type

conf

DOI

10.1109/IGARSS.2013.6723734

Filename

6723734