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文件名称:A Discriminative Metric Learning Based Anomaly Detection Method
文件大小:2.16MB
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更新时间:2022-01-10 10:29:22
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Abstract—Due to the high spectral resolution, anomaly detection
from hyperspectral images provides a new way to locate
potential targets in a scene, especially those targets that are spectrally
different from the majority of the data set. Conventional
Mahalanobis-distance-based anomaly detection methods depend
on the background statistics to construct the anomaly detection
metric. One of the main problems with these methods is that the
Gaussian distribution assumption of the background may not be
reasonable. Furthermore, these methods are also susceptible to
contamination of the conventional background covariance matrix
by anomaly pixels. This paper proposes a new anomaly detection
method by effectively exploiting a robust anomaly degree metric
for increasing the separability between anomaly pixels and other
background pixels, using discriminative information. First, the
manifold feature is used so as to divide the pixels into the potential
anomaly part and the potential background part. This procedure
is called discriminative information learning. A metric learning
method is then performed to obtain the robust anomaly degree
measurements. Experiments with three hyperspectral data sets reveal
that the proposedmethod outperforms other current anomaly
detection methods. The sensitivity of