Title
Gas plume detection and tracking in hyperspectral video sequences using Binary Partition Trees
Abstract
Thanks to the fast development of sensors, it is now possible to acquire sequences of hyperspectral images. Those hyperspectral video sequences are particularly suited for the detection and tracking of chemical gas plumes. However, the processing of this new type of video sequences with the additional spectral diversity, is challenging and requires the design of advanced image processing algorithms. In this paper, we present a novel method for the segmentation and tracking of a chemical gas plume diffusing in the atmosphere, recorded in a hyperspectral video sequence. In the proposed framework, the position of the plume is first estimated, using the temporal redundancy of two consecutive frames. Second, a Binary Partition Tree is built and pruned according to the previous estimate, in order to retrieve the real location and extent of the plume in the frame. The proposed method is validated on a real hyperspectral video sequence and compared with a state-of-the-art method.
Year
DOI
Venue
2014
10.1109/WHISPERS.2014.8077581
2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Keywords
DocType
ISSN
segmentation,tracking,Binary Partition Tree,chemical gas plume,hyperspectral video sequence
Conference
2158-6268
ISBN
Citations 
PageRank 
978-1-4673-9013-2
6
0.78
References 
Authors
8
6
Name
Order
Citations
PageRank
Guillaume Tochon1506.43
Jocelyn Chanussot24145272.11
Jérôme Gilles3815.87
Mauro Dalla Mura487556.48
Jen-Mei Chang5423.26
Andrea L. Bertozzi648661.55