D. Stein

Modeling variability in hyperspectral imagery using a stochastic compositional approach

D. Stein, 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

Anomaly detection from hyperspectral imagery

E. M. Winter, D. Stein, A. Schaum, 2002, IEEE Signal Process. Mag..

STATISTICAL CHARACTERISTICS OF MOVING ACOUSTIC SOURCES IN OCEAN WAVEGUIDES

D. Stein, 1995 .

Hyperspectral Imaging for Intelligence, Surveillance, and Reconnaissance

D. Stein, J. Schoonmaker, E. Coolbaugh, 2001 .

A robust exponential mixture detector applied to radar

D. Stein, 1999 .

Detection of random signals in Gaussian mixture noise

D.W.J. Stein, D. Stein, 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

The Fusion of Quadratic Detection Statistics Applied to Hyperspectral Imagery

D. Stein, A. Stocker, Scott Deaven, 2001 .

Application of the normal compositional model to the analysis of hyperspectral imagery

D. Stein, D. Stein, David Stein, 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.

Computing K-clutter-plus-Gauss distributions

D.W.J. Stein, D. Stein, 1997 .

Adaptive Locally Optimum Processing for Interference Suppression from Communication and Undersea Surveillance Signals

Stefen Hui, David Stein, James W. Bond, 1994 .

Generation of synthetic HSI data using linear mixing model with Gaussian endmembers

Dimitris G. Manolakis, David Walter Jacques Stein, D. Manolakis, 2004, SPIE Defense + Commercial Sensing.

Material identification and classification in hyperspectral imagery using the normal compositional model

David Walter Jacques Stein, D. Stein, 2003, SPIE Defense + Commercial Sensing.

Comparison of Gaussian mixture and linear mixture models for classification of hyperspectral data

S. G. Beaven, D.W.J. Stein, L. E. Hoff, 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).