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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