Anomaly detection with sparse unmixing and gaussian mixture modeling of hyperspectral images
Author(s)
Advisor
Aksoy, SelimDate
2015-07Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
One of the main applications of hyperspectral image analysis is anomaly detection
where the problem of interest is the detection of small rare objects that
stand out from their surroundings. A common approach to anomaly detection is
to rst model the background scene and then to use a detector that quanti es
the di erence of a particular pixel from this background. However, identifying
the dominant background components and modeling them is a challenging task.
We propose an anomaly detection framework that uses Gaussian mixture models
for characterizing the scene background in hyperspectral images. First, the full
spectrum is divided into several contiguous band groups for dimensionality reduction
as well as for exploiting the peculiarities of di erent parts of the spectrum.
Then, sparse spectral unmixing is performed for each band group for identifying
signi cant endmembers in the scene. Three methods for identifying the dominant
background groups such as thresholding, hierarchical clustering and biclustering
are used in the endmember abundance space to retrieve the sets of pixel groups
that represent dominant background components. Next, these pixel groups are
used for initializing individual Gaussian mixture models that are estimated separately
for each spectral band group. The proposed method enables automatic
identi cation of the number of mixture components and e ective initialization
of the estimation procedure for the mixture model. Finally, the Gaussian mixture
models for all groups are statistically fused for obtaining the nal anomaly
map for the scene. Comparative experiments showed that the proposed methods
performed better than two other density-based anomaly detectors, especially for
small false positive rates, on an airborne hyperspectral data set.
Keywords
Anomaly detectionSpectral unmixing
Gaussian mixture model
Hierarchical clustering
Biclustering
Hyperspectral imaging