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Abstract:
In this paper we examine the efficacy of using the closest distance to center algorithm in conjunction with ellipsoidal multivariate trimming (MVT) to find outliers in a hyperspectral image. MVT is applied here as a global anomaly detector on images that are pre-processed into clusters using a technique called X-means. Under the assumption that there are no more than 5% outliers in any given cluster set, we develop a method, based upon principal component analysis preprocessing to create a flexible threshold for determining the percentage of data to retain with MVT. Using a retention percentage that more adequately reflects the actual number of outlier-free observations allows one to form estimates of the mean and covariance matrix that more effectively decrease the effects of swamping and masking as compared to using a set percentile for retention. These ideas are tested against real and synthetically generated hyperspectral imagery.
| Limitations: |
 APPROVED FOR PUBLIC RELEASE |
| Description: |
Journal article |
| Pages: |
12 |
| Report Date: |
2011 |
| Report Number: |
A992645 |
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