Wavelength band selection method for multispectral target detection.

A framework is proposed for the selection of wavelength bands for multispectral sensors by use of hyperspectral reference data. Using the results from the detection theory we derive a cost function that is minimized by a set of spectral bands optimal in terms of detection performance for discrimination between a class of small rare targets and clutter with known spectral distribution. The method may be used, e.g., in the design of multispectral infrared search and track and electro-optical missile warning sensors, where a low false-alarm rate and a high-detection probability for detection of small targets against a clutter background are of critical importance, but the required high frame rate prevents the use of hyperspectral sensors.

[1]  J. C. Price,et al.  Band selection procedure for multispectral scanners. , 1994, Applied optics.

[2]  B. Rao,et al.  Forward sequential algorithms for best basis selection , 1999 .

[3]  R. O. Johnson,et al.  Band selection and performance analysis for multispectral target detectors using truthed Bomem spectrometer data , 1996, Proceedings of the IEEE 1996 National Aerospace and Electronics Conference NAECON 1996.

[4]  Russell C. Hardie,et al.  Spectral band selection and classifier design for a multispectral imaging laser radar , 1998 .

[5]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[6]  Dimitris G. Manolakis,et al.  Detection algorithms for hyperspectral imaging applications , 2002, IEEE Signal Process. Mag..

[7]  Zhifeng Zhang,et al.  Adaptive time-frequency decompositions , 1994 .

[8]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[9]  S. Mallat,et al.  Adaptive greedy approximations , 1997 .

[10]  A. Berk MODTRAN : A moderate resolution model for LOWTRAN7 , 1989 .

[11]  Dimitris G. Manolakis,et al.  Comparative analysis of hyperspectral adaptive matched filter detectors , 2000, SPIE Defense + Commercial Sensing.

[12]  Chein-I Chang,et al.  Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach , 1994, IEEE Trans. Geosci. Remote. Sens..

[13]  Balas K. Natarajan,et al.  Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..