Oblique hyperspectral target detection

This work investigates target detection using simulated hyperspectral imagery captured from highly oblique angles. This paper seeks to determine which domain, radiance or reflectance, is more appropriate for the off-nadir case. An oblique atmospheric compensation technique based on the empirical line method (ELM) is presented and used to compensate the simulated data used in this study. The resulting reflectance cubes are subjected to a variety of standard target detection processes. A forward modeling technique that is appropriate for use on oblique hyperspectral data is also presented. This forward modeling process allows for standard target detection techniques to be applied in the radiance domain. Results obtained from the radiance and reflectance domains are comparable. Under ideal circumstances, however, the radiance domain results are slightly better than the results observed in the reflectance domain. These somewhat favorable results for the radiance domain, considered with the practicality and potential operational applicability of the forward modeling technique presented, suggest that the radiance domain is an attractive option for oblique hyperspectral target detection.

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

[2]  Emmett J. Ientilucci,et al.  Hyperspectral sub-pixel target detection using hybrid algorithms and Physics Based Modeling , 2005 .

[3]  Steven M. Adler-Golden,et al.  Atmospheric compensation of extreme off-nadir hyperspectral imagery from Hyperion , 2007, SPIE Defense + Commercial Sensing.

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

[5]  John R. Schott,et al.  Target Detection in a Structured Background Environment Using an Infeasibility Metric in an Invariant Space , 2005 .

[6]  Alan P. Schaum,et al.  Signature evolution with covariance equalization in oblique hyperspectral imagery , 2006, SPIE Defense + Commercial Sensing.

[7]  Kris E. Barcomb,et al.  High-resolution slant-angle scene generation and validation of concealed targets in DIRSIG , 2004, SPIE Optics + Photonics.

[8]  Glenn Healey,et al.  Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions , 1999, IEEE Trans. Geosci. Remote. Sens..

[9]  Scott D. Brown,et al.  Advances in wide-area hyperspectral image simulation , 2003, SPIE Defense + Commercial Sensing.

[10]  John R. Schott,et al.  Comparison of basis-vector selection methods for target and background subspaces as applied to subpixel target detection , 2004, SPIE Defense + Commercial Sensing.

[11]  John R. Schott,et al.  Physics Based Target Detection Using a Hybrid Algorithm With an Infeasibility Metric , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[12]  Harry N. Gross,et al.  An Advanced Synthetic Image Generation Model and its Application to Multi/Hyperspectral Algorithm Development , 1999 .

[13]  D. C. Robertson,et al.  MODTRAN: A Moderate Resolution Model for LOWTRAN , 1987 .

[14]  B. Krauskopf,et al.  Proc of SPIE , 2003 .

[15]  David C. Grimm.  Comparison of Hyperspectral Imagery Target Detection Algorithm Chains , 2005 .

[16]  Peter Bajorski,et al.  Statistical models for physically derived target sub-spaces , 2006, SPIE Optics + Photonics.

[17]  Gary A. Shaw,et al.  Hyperspectral Image Processing for Automatic Target Detection Applications , 2003 .

[18]  Glenn Healey,et al.  The impact of viewing geometry on vision through the atmosphere , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[19]  Michelle L. Aten,et al.  Stable band set selection for the preprocessing of AVIRIS and Hyperion hyperspectral data , 2004, SPIE Remote Sensing.