Imaging Science Seminar: Dr. Stanley Rotman
Imaging Science Seminar
Segmentation for Point Target Detection in Hyperspectral Data
Dr. Stanley Rotman
Ben-Gurion University of the Negev
The speaker will discuss issues with hyperspectral target detection using segmentation and discuss ways to improve detector performance.
Abstract: Point target detection algorithms in hyperspectral imaging commonly use the spectral inverse covariance matrix to whiten the natural noise of the image. Since the noise in hyperspectral data cubes often suffer from a lack of stationarity, segmentation appears to be an attractive preprocessing operation. However, the literature contains examples of successful and unsuccessful segmentation with no plausible explanation for why some succeed, and others do not. Focusing on one representative algorithm and assuming a target additive model, this presentation tracks the underlying causes of when segmentation does improve detection for different target spectra. It then characterizes a real dataset and concludes with ways to improve the detector performance.
Speaker Bio: Stanley R. Rotman is originally from Boston, Massachusetts. He received B.S., M.S. and Ph.D. degrees in Electrical Engineering from the Massachusetts Institute of Technology, in 1979, 1980 and 1985, respectively. His present position is full professor at Ben-Gurion University of the Negev, Department of Electrical and Computer Engineering, Beer-Sheva, Israel. His main research areas are: modeling target acquisition by infrared sensors, spectroscopy of solid-state laser material, investigating non-radiative energy transfer in solid-state materials. medical image processing and digital image processing. His recent publications are titled "Damage Assessment in Rural Environments Following Natural Disasters Using Multi-Sensor Remote Sensing Data", "Machine Learning for Detecting Anomalies in SAR Data", "Point Target Detection Using Nonnegative Matrix Factorization", and "Hyperspectral Target Detection Using Tree-Structured Probabilistic Graphical Model and Semi-Parametric Density Estimation."
Intended Audience: Undergraduates, graduates, and experts. Those with interest in the topic.
Event Snapshot
When and Where
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Open to the Public
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No