IEEE Systems, Man and Cybernetics Magazine - July 2019 - 64

repeat-pass snippets through the false-alarm reduction step.
Notice that the three changes were identified correctly.

Figure 6. Changes were identified using the PCA-ICA

false alarm reduction method.

incoherent change maps. If an object was inserted into
the scene after the initial pass, a dark intensity appears
in the incoherent change map where the new object was
introduced. However, if an object was removed, a bright
intensity appears in the incoherent change map where
the object was deleted. Finally, when an object remains
stationary, the ICD change map is gray, since nothing in
the scene has changed. Therefore, the presence of bright
or dark intensities will require adaptable thresholding of
the ICD change map.
Currently, various methods, including standard variation-based and zero-detect threshold [26], are being examined for ICD change-map analysis. Figure 5 displays the
detected snippets from the ICD change map that correspond to the repeat-pass image. The corresponding snippets
from the initial-pass image are not shown.
False-Alarms Reduction
In the final stage of the ACD-processing stream, false-alarm
reduction aims to eliminate anomalies due to noise. Because
high false-alarm detection rates are the foremost problem for
the automated change-detection process, this final stage is
necessary for an effective detector. Principal and independent component analyses (PCA and ICA, respectively) are
used to accomplish false-alarm reduction. Component analysis (CA) uses statistics as a criterion to decorrelate and convert data into a set of uncorrelated data components for
study. ICA is a high-order statistics-based CA that involves
criteria such as third-order statistics, skewness, and the
fourth-order statistics kurtosis. The drawback of PCA is the
use of second-order statistics, which might not capture weak
targets. On the other hand, higher-order statistics-based component transforms, which include skewness and kurtosis,
could reliably reduce false alarms. As a result, the high-order
statistic-based prioritized ICA method detailed in [27] is used
to reduce false alarms. Figure 6 demonstrates the remaining
detected snippets because of filtering the reference and
64

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Ju ly 2019

ACD Future Capabilities
ACD implies that target detection can be achieved automatically, based on information collected by imaging sensors. Because it is difficult for a detector to account for
every operational condition, developing ACD techniques
consider a subset of environments. Consequently, the performance of the ACD process may not be consistent across
other operational conditions. Thus, with developing ACD
techniques and applications, evaluating the performance
of ACD is becoming an important subject for changedetection theory in the community.
The current framework shown in Figure 2 will extend
to exploit multitemporal data and sensor performance
assessment as a function of environmental conditions and
the frequency of repeat passes. To complement the recent
maturation of ACD techniques, involving the human operator in the development is a necessary next step. Ultimately,
ACD is intended to reduce the workload of the human
operator. Therefore, the requirement of a decision aid to
predict operation time scales of utility for ACD will need to
be addressed.
Conclusion
In this article, we presented an automated end-to-end processing stream of change detection using multitemporal
SAS sea-floor imagery. Although the ACD process was
applied to SAS imagery, it certainly could be applied to
other imaging modalities. Furthermore, we discussed concepts of advancing the ACD process for operator decision
aids. In particular, the next step toward reducing the workload of the operator would be to include the exploitation of
available data, environmental descriptors, and sensor
characteristics to evaluate the performance and effectiveness prediction of ACD techniques.
Acknowledgment
We would like to thank Dr. D. Cook, the Office of Naval
Research Code 321OE, Ocean Engineering and Maritime
Systems, and the U.S. Navy International Programs Office
for their guidance and support.
About the Authors
Tesfaye G-Michael (tesfaye.g-michael@boeing.com)
earned his B.S. degree in biomedical engineering, his
M.S. and M.Sc. degrees in electrical and computer engineering from Wright State University, Fairborn, Ohio,
and his Ph.D. degree in electrical engineering from Florida State University, Tallahassee. He is a senior radar systems engineer with Boeing. His research interests
include radar and sonar signal and image processing,
automated change detection, machine learning and
applications, and big data analytics. He is a Senior Member of the IEEE.



IEEE Systems, Man and Cybernetics Magazine - July 2019

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