Graph MBO method for multiclass segmentation of hyperspectral stand-off detection video

Author

Merkurjev, Ekaterina ; Sunu, Justin ; Bertozzi, Andrea L.

Author_Institution

Dept. of Math., Univ. of California, Los Angeles, Los Angeles, CA, USA

fYear

2014

fDate

27-30 Oct. 2014

Firstpage

689

Lastpage

693

Abstract

We consider the challenge of detection of chemical plumes in hyperspectral image data. Segmentation of gas is difficult due to the diffusive nature of the cloud. The use of hyperspectral imagery provides non-visual data for this problem, allowing for the utilization of a richer array of sensing information. In this paper, we present a method to track and classify objects in hyperspectral videos. The method involves the application of a new algorithm recently developed for high dimensional data. It is made efficient by the application of spectral methods and the Nyström extension to calculate the eigenvalues/eigenvectors of the graph Laplacian. Results are shown on plume detection in LWIR standoff detection.

Keywords

geophysical image processing; graph theory; hyperspectral imaging; image segmentation; object tracking; remote sensing; video signal processing; LWIR standoff detection; Nyström extension; chemical plumes; graph MBO method; hyperspectral image data; hyperspectral stand-off detection video; multiclass segmentation; object classification; object tracking; spectral methods; Chemicals; Eigenvalues and eigenfunctions; Hyperspectral imaging; Laplace equations; Sensors; Video sequences; MBO scheme; Nyström extension method; classification; hyperspectral data; tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing (ICIP), 2014 IEEE International Conference on

Conference_Location

Paris

Type

conf

DOI

10.1109/ICIP.2014.7025138

Filename

7025138