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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 19, Iss. 21 — Oct. 10, 2011
  • pp: 20916–20929

Object level HSI-LIDAR data fusion for automated detection of difficult targets

A. V. Kanaev, B. J. Daniel, J. G. Neumann, A. M. Kim, and K. R. Lee  »View Author Affiliations

Optics Express, Vol. 19, Issue 21, pp. 20916-20929 (2011)

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Data fusion from disparate sensors significantly improves automated man-made target detection performance compared to that of just an individual sensor. In particular, it can solve hyperspectral imagery (HSI) detection problems pertaining to low-radiance man-made objects and objects in shadows. We present an algorithm that fuses HSI and LIDAR data for automated detection of man-made objects. LIDAR is used to define a set of potential targets based on physical dimensions, and HSI is then used to discriminate between man-made and natural objects. The discrimination technique is a novel HSI detection concept that uses an HSI detection score localization metric capable of distinguishing between wide-area score distributions inherent to natural objects and highly localized score distributions indicative of man-made targets. A typical man-made localization score was found to be around 0.5 compared to natural background typical localization scores being less than 0.1.

© 2011 OSA

OCIS Codes
(280.0280) Remote sensing and sensors : Remote sensing and sensors
(280.4788) Remote sensing and sensors : Optical sensing and sensors

ToC Category:
Remote Sensing

Original Manuscript: June 2, 2011
Revised Manuscript: September 9, 2011
Manuscript Accepted: September 15, 2011
Published: October 6, 2011

A. V. Kanaev, B. J. Daniel, J. G. Neumann, A. M. Kim, and K. R. Lee, "Object level HSI-LIDAR data fusion for automated detection of difficult targets," Opt. Express 19, 20916-20929 (2011)

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  1. I. S. Reed and X. Yu, “Adaptive Multiple-Band CFAR Detection of an Optical Pattern with Unknown Spectral Distribution,” IEEE Trans. Acoust. Speech Signal Process. 38, 10 (1992).
  2. D. W. J. Stein, S. C. Beaven, L. E. Hoff, E. W. Winter, A. P. Schaum, and A. D. Stocker, “Anomaly detection from hyperspectral imagery,” IEEE Signal Process. Mag. 19(1), 58–69 (2002). [CrossRef]
  3. D. Manolakis, “Taxonomy of detection algorithms for hyperspectral imaging applications,” Opt. Eng. 44(6), 066403 (2005). [CrossRef]
  4. A. V. Kanaev and J. Murray-Krezan, “Spectral anomaly detection in deep shadows,” Appl. Opt. 49(9), 1614–1622 (2010). [CrossRef] [PubMed]
  5. M. A. Kolodner, “Automated target detection system for hyperspectral imaging sensors,” Appl. Opt. 47(28), F61–F70 (2008). [CrossRef] [PubMed]
  6. M. S. Foster, J. R. Schott, D. W. Messinger, and R. Raqueno, “Use of Lidar data to geometrically-constrain radiance spaces for physics-based target detection,” Proc. SPIE 6661, 66610J (2007). [CrossRef]
  7. D. E. Bar, K. Wolowelsky, Y. Swirski, Z. Figov, A. Michaeli, Y. Vaynzof, Y. Abramovitz, A. Ben-Dov, O. Yaron, L. Weizman, and R. Adar, “Target Detection and Verification via Airborne Hyperspectral and High-Resolution Imagery Processing and Fusion,” IEEE Sens. J. 10(3), 707–711 (2010). [CrossRef]
  8. M. Dalponte, L. Bruzzone, and D. Gianelle, “Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas,” IEEE Trans. Geosci. Rem. Sens. 46(5), 1416–1427 (2008). [CrossRef]
  9. D. Borghys, M. Shimoni, and C. Perneel, “Change detection in urban scenes by fusion of SAR and hyperspectral data,” Proc. SPIE 6749, 67490R (2007). [CrossRef]
  10. S. Kraut, L. Scharf, and L. T. McWhorter, “Adaptive sub-space detectors,” IEEE Trans. Signal Process. 49(1), 1–16 (2001). [CrossRef]
  11. A. P. Schaum and A. D. Stocker, “Hyperspectral change detection and supervised matched filtering based on Covariance Equalization,” Proc. SPIE 5425, 77–90 (2004). [CrossRef]
  12. G. A. F. Seber, Multivariate Observations (John Wiley & Sons Inc., 1984).
  13. MATLAB version R2011a. Natick, MA The MathWorks Inc., 2011.
  14. F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Trans. Med. Imaging 16(2), 187–198 (1997). [CrossRef] [PubMed]

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