Multisensor data fusion for surface land-mine detection

IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

Volumn 30, Issue 1, 2000, Pages 145-150

  Filippidis, Arthur ^a   Jain, L C ^a   Martin, N ^a  

^a DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BOMBS (ORDNANCE); CAMERAS; FEATURE EXTRACTION; HOUGH TRANSFORMS; IMAGE ENHANCEMENT; IMAGE SEGMENTATION; INFRARED DETECTORS; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; NEURAL NETWORKS; RADAR TARGET RECOGNITION;

FALSE ALARM RATES; LAND MINE DETECTION; RECEIVER OPERATING CURVES;

SENSOR DATA FUSION;

EID: 0033732789     PISSN: 10946977     EISSN: None     Source Type: Journal    
DOI: 10.1109/5326.827491     Document Type: Article

References (12)

1. B. A. Babour, S. Kordella, M. J. Dorsett, and B. L. Kerstiens, "Mine detection using a polarimetric IR sensor," presented at the Detection of Abandoned Land-Mine, 1996, IEE, Conf. Pub. Paper 431.
2. N. Stacy, R. Smith, and G. Nash, "Automatic target recognition for the INGARRA airborne radar surveillance system," Defence Science Technology Organization, Salisbury, Australia, Microwave Radar Division Internal Rep., Aug. 1994.
3. M. Bower, E. Cloud, H. Duvoisin, D. Long, and J. Hackett, Development of Automatic Target Recognition for Infrared Sensor-Based Close Range Land-Mine Detector. Orlando, FL: Martin Marietta, 1995.
4. K. Fuelop and J. Hall, "Thermal infrared land-mine detection," Defence Science Technology Organization, Salisbury, Australia, Tech. Rep. DSTO-TR-0295 AR-009-485, Jan. 1996.
5. G. Carpenter and S. Grossberg, Pattern Recognition by Self-Organizing Neural Networks, New York: Academic, 1987, pp. 399-410.
6. A. Filippidis, L. C. Jain, and P. Lozo, "Degree of familiarity ART2 in fuzzy fusion landmine detection," in Int. Conf. Knowledge-Based Intelligent Electronic Systems (KES'98), Adelaide, Australia, Apr. 1998, pp. 359-363.
7. R. M. Harlick, K. Shunmuhham, and I. Distein, "Textural features for image classification," IEEE Trans. Syst., Man, Cybern., vol. SMC-3, Nov. 1973.
8. P. J. Whitbread, "Multispectral texture," Ph.D. dissertation, Univ. of Adelaide, Australia, Oct. 1992.
9. V. K. Shettigara, S. G. Kempinger, and E. Kruzins, "Fusion of texture and intensity characteristics for target detection in SAR images," in P roc. Int. Workshop IAIF'97: Image Analysis and Information Fusion, Adelaide, South Australia, 1997, pp. 495-499.
10. P. P. Ohanian and R. C. Dubes, "Performance evaluation for four classes of textural features," Pattern Recognition, vol. 25, pp. 819-833, 1992.
11. Neuro Genetic Optimizer Version 32202. Redmond, WA: BioComp.
12. A. Filippidis and L. C. Jain, "Identity attribute information in a multi-band aerial photo image using neural networks to automatically detect targets," Int. J. Knowledge-Based Intell. Eng. Syst., vol. 1, pp. 175-181, Jan. 1997.