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

Optics Express

Volumn 19, Issue 21, 2011, Pages 20916-20929

  Kanaev, A V ^a   Daniel, B J ^a   Neumann, J G ^a   Kim, A M ^b   Lee, K R ^b  

^a NAVAL RESEARCH LABORATORY   (United States)

^b NAVAL POSTGRADUATE SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATION; IMAGE SEGMENTATION; INFORMATION FUSION; OPTICAL RADAR; REMOTE SENSING;

AUTOMATED DETECTION; DETECTION PROBLEMS; HYPERSPECTRAL IMAGERY; LIDAR DATA; MAN MADE OBJECTS; NATURAL BACKGROUNDS; NATURAL OBJECTS; PHYSICAL DIMENSIONS; WIDE AREA;

SENSOR DATA FUSION;

ALGORITHM; ARTICLE; CLUSTER ANALYSIS; COMPUTER PROGRAM; ENTROPY; INFORMATION PROCESSING; OPTICS; REPRODUCIBILITY; SIGNAL PROCESSING; STATISTICAL MODEL;

ALGORITHMS; AUTOMATIC DATA PROCESSING; CLUSTER ANALYSIS; ENTROPY; MODELS, STATISTICAL; OPTICS AND PHOTONICS; REPRODUCIBILITY OF RESULTS; SIGNAL PROCESSING, COMPUTER-ASSISTED; SOFTWARE;

EID: 80053944633     PISSN: None     EISSN: 10944087     Source Type: Journal    
DOI: 10.1364/OE.19.020916     Document Type: Article

References (14)

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). (Pubitemid 34237208)
3. D. Manolakis, "Taxonomy of detection algorithms for hyperspectral imaging applications", Opt. Eng. 44(6), 066403 (2005).
4. A. V. Kanaev and J. Murray-Krezan, "Spectral anomaly detection in deep shadows", Appl. Opt. 49(9), 1614-1622 (2010).
5. M. A. Kolodner, "Automated target detection system for hyperspectral imaging sensors", Appl. Opt. 47(28), F61-F70 (2008).
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).
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).
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).
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).
10. S. Kraut, L. Scharf, and L. T. McWhorter, "Adaptive sub-space detectors", IEEE Trans. Signal Process. 49(1), 1-16 (2001).
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).
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). (Pubitemid 127762930)