Applying ISOMAP to the learning of hyperspectral image
Wang, X. Rosalind., Kumar, Suresh, Kaupp, Tobias., Upcroft, Ben, & Durrant-Whyte, Hugh (2005) Applying ISOMAP to the learning of hyperspectral image. In Sammut, Claude. (Ed.) Proceedings of the 2005 Australasian Conference on Robotics & Automation, Australian Robotics & Automation Association, Sydney, N.S.W.
In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.
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|Item Type:||Conference Paper|
|Keywords:||ISOMAP, hyperspectral image|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2005 [please consult author]|
|Deposited On:||10 May 2011 12:30|
|Last Modified:||11 Aug 2011 06:27|
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