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.
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Communication dans un congrès
IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2014), Jun 2014, Lausanne, Switzerland
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http://hal.univ-grenoble-alpes.fr/hal-01052719
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Dernière modification le : mercredi 19 septembre 2018 - 01:15:06
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  • HAL Id : hal-01052719, version 1

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Guillaume Tochon, Jocelyn Chanussot, Jérôme Gilles, Mauro Dalla Mura, Jen-Mei Chang, et al.. Gas Plume Detection and Tracking in Hyperspectral Video Sequences using Binary Partition Trees. IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2014), Jun 2014, Lausanne, Switzerland. 〈hal-01052719〉

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