[CSEE-colloq] CSEE Colloquium begins in a few minutes - at 1 pm - Yu-Lei Wang on Real-time Causal Anomaly Detection for Hyperspectral Imagery - in ITE 227, UMBC

[Yaacov Yesha]

Dear recipients,

CSEE Colloquium begins in a few minutes -  at 1 pm - Yu-Lei Wang on
Real-time Causal Anomaly Detection for Hyperspectral Imagery - in ITE 227,
UMBC.

I hope that you will attend.

Thanks.

Yaacov
---------- Forwarded message ----------
From: Tim Finin <finin at cs.umbc.edu>
Date: Mon, Oct 8, 2012 at 9:10 PM
Subject: [Csee-faculty-tt] [CSEE-colloq] talk: Real-time Causal Anomaly
Detection for Hyperspectral Imagery, 1pm 10/12, UMBC
To: csee-colloquium-out at cs.umbc.edu


                      UMBC CSEE Colloquium

  Real-time Causal Anomaly Detection for Hyperspectral Imagery

                   Yu-Lei Wang Information and
            Communication Engineering College Harbin
                  Engineering University, China

          1:00pm Friday, 12 October 2012, ITE 227, UMBC

Due to availability of very high spectral resolution, a hyper-
spectral imaging sensor is capable of uncovering many subtle
signal sources which cannot be visually inspected or known by
prior knowledge. Such signal sources generally appear as
anomalies in the data. As a result, anomaly detection has
received considerable interest in hyperspectral imaging. In
anomaly detection real time causal processing is particularly
important and crucial. This is because many anomalies, such as
moving targets, may not stay long enough and the duration of
their presence is very short. Most importantly, they may show up
suddenly and instantly, then disappear quickly afterwards.
Therefore, for an algorithm to be able to detect these targets in
a timely fashion, the process must be real time. In addition, the
data that can be used should be only those which have been
visited and processed. So, the data processing must be also
causal as well. Such causality is a very important pre-requisite
to real time processing. Our work is believed to be the first
work devoted to exploring this concept into anomaly detection.
Specifically, it further derives a causal innovations information
update equation for implementing real time causal anomaly
detection. This concept which makes use of only innovations
information provided by the pixel currently being processed
without re-processing previous pixels is similar to those derived
in Kalman filtering.

Yu-Lei Wang received her BS degree in Electrical Engineering from
Harbin Engineering University, China in 2009 and is currently a
Ph.D. student in the same university. Since December 2011
Ms. Wang has been working in the Remote Sensing Signal and Image
Processing Laboratory at UMBC on hyperspectral anomaly detection
under a China State Scholarship awarded by China Scholarship
Council for a two-year visit to UMBC. Ms. Wang's research
interest includes remote sensing image processing and vital sign
signal processing.

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