Laplacian eigenmaps manifold learning and anomaly detection methods for spectral images

Show simple item record

dc.contributor.advisor Basener, William Munoz Reales, Marcela 2011-03-08T15:25:10Z 2011-03-08T15:25:10Z 2010-11-16
dc.description.abstract Spectral images provide a large amount of spectral information about a scene, but sometimes when studying images, we are interested in specific components. It is a difficult problem to separate the relevant information or what we call interesting from the background of a spectral image, even more so if our target objects are unknown. Anomaly detection is a process by which algorithms are designed to separate the anomalous (different) points from the background of an image. The data is complex and lives in a high dimension, manifold learning algorithms are used to analyze data that lives in a high dimensional space, but that can be represented as a lower dimensional manifold embedded in the high dimensional space. Laplacian Eigenmaps is a manifold learning algorithm that applies spectral graph theory to perform a non-linear dimensionality reduction that preserves local neighborhood information. We present an approach to reduce the dimension of the data and separate anomalous pixels in spectral images using Laplacian Eigenmaps. en_US
dc.language.iso en_US en_US
dc.relation RIT Scholars content from RIT Digital Media Library has moved from to RIT Scholar Works, please update your feeds & links!
dc.subject Remote sensing en_US
dc.subject.lcc TA1637 .M86 2010 en_US
dc.subject.lcsh Remote sensing--Mathematics en_US
dc.subject.lcsh Remote sensing--Data processing en_US
dc.subject.lcsh Spectral theory (Mathematics) en_US
dc.subject.lcsh Graph theory en_US
dc.subject.lcsh Laplacian operator en_US
dc.subject.lcsh Machine learning en_US
dc.title Laplacian eigenmaps manifold learning and anomaly detection methods for spectral images en_US
dc.type Thesis en_US College of Science en_US
dc.description.department School of Mathematical Sciences en_US

Files in this item

Files Size Format View
MMunozRealesThesis11-16-2010.pdf 856.1Kb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

Search RIT DML

Advanced Search