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Feature extraction techniques for LIDAR range profile based object recognition
F.B. Baulin 1, E.V. Buryi 1

1 Bauman Moscow State Technical University (National Research University)

 PDF, 1583 kB

DOI: 10.18287/2412-6179-CO-891

Pages: 934-941.

Full text of article: Russian language.

Abstract:
The article provides an overview of range profile feature extraction methods used in laser iden-tification, detection and ranging systems. It also outlines feature selection methods and highlights their respective limitations. A novel feature selection method which maximizes Euclidian dis-tances between feature vectors is presented. The article also showcases advantages of the proposed technique by extracting features of basic objects (a sphere, a cone, and a cylinder). This method is shown to be effective when feature vector manifolds are not linearly separable due to the unknown viewing aspect of an object. The technique is also effective when feature vector manifolds overlap due to the different objects having similar range profiles.

Keywords:
lidar, laser sensor, backscattering, range profile, pattern recognition, wavelets, feature extraction, feature selection.

Citation:
Baulin FB, Buryi EV. Feature extraction techniques for LIDAR range profile based object recognition. Computer Optics 2021; 45(5): 934-941. DOI: 10.18287/2412-6179-CO-891.

References:
  1. Buryi EV. Pulse LIDARs: physical and informational base of new capabilities [In Russian]. Moscow: "Nauka" Publisher; 2020. ISBN: 978-5-02-040772-5.
  2. Baum J, Tung E, Rak S. Non-cooperative identification of ships with electrooptical data. Linc Lab J 1994; 7(1): 3-30.
  3. Marino RM, Davis WR. Jigsaw: a foliage-penetrating 3D imaging laser radar system. Linc Lab J 2005; 15 (1): 23-36.
  4. Vasile, AN, Marino, RM Pose-independent automatic target detection and recognition using 3D laser radar imagery. Linc Lab J 2005; 15(1): 61-78. DOI: 10.1117/12.546761.
  5. van den Heuvel JC, Schoemaker RM, Schleijpen RHMA. Identification of air and sea-surface targets with a laser range profiler. Proc SPIE 2009; 7323: 73230Y. DOI: 10.1117/12.818426.
  6. Schoemaker, RM, Benoist KW. Characterisation of small targets in a maritime environment by means of laser range profiling. Proc SPIE 2011; 8037: 803705. DOI: 10.1117/12.884575.
  7. Steinvall O, Tulldahl M. Laser range profiling for small target recognition. Opt Eng 2017; 56(3): 031206. DOI: 10.1117/1.OE.56.3.031206.
  8. Buryi EV. Synthesis of an object recognition system based on the profile of the envelope of a laser pulse in pulsed lidars. Quantum Electronics 1998; 25(5): 458-462. DOI: 10.1070/QE1998v028n05ABEH001248.
  9. Baulin F, Buryi E, Semerenko D. Efficiency analysis of feature extraction methods for pulse laser ranging systems. PIERS 2017: 3790-3794. DOI: 10.1109/PIERS.2017.8262418.
  10. Hofton MA, Minster JB, Blair JB. Decomposition of laser altimeter waveforms. IEEE Trans Geosci Remote Sens 2000; 38(4): 1989-1996. DOI: 10.1109/36.851780.
  11. Hancock S, Anderson K, Disney M, Gaston KJ. Measurement of fine-spatial-resolution 3D vegetation structure with airborne waveform lidar: Calibration and validation with voxelised terrestrial lidar. Remote Sens Environ 2017; 188: 37-50. DOI: 10.1016/j.rse.2016.10.041.
  12. Azadbakht M, Fraser C, Khoshelham K. A sparsity-based regularization approach for deconvolution of full-waveform airborne lidar data. Remote Sens 2016, 8(8): 648. DOI: 10.3390/rs8080648.
  13. Steinvall O, Tulldahl M, Berglund F, Allard L. Laser profiling for airborne target classification. Proc SPIE 2018; 10636: 1063602. DOI: 10.1117/12.2303965.
  14. Steinvall O, Ericson B. Remote detection and size estimation of optical apertures. Proc SPIE 2019; 11161: 111610I. DOI: 10.1117/12.2533035.
  15. Baulin FB, Buryi EV. Performance estimate of range profile feature extraction for the case of defined viewing aspect by means of fisher score. PIERS-Spring 2019: 2922-2926. DOI: 10.1109/PIERS-Spring46901.2019.9017824.
  16. Baulin FB, Buryi EV. Performance estimate of range profile feature extraction by means of interclass metric histograms analysis. PIERS-Spring 2019: 2933-2937. DOI: 10.1109/PIERS-Spring46901.2019.9017782.
  17. Marple SL. Digital spectral analysis: With applications. Upper Saddle River, NJ: Prentice-Hall Inc; 1986. ISBN: 978-0-13-214149-9.
  18. Daubechies I. Ten lectures on wavelets. Philadelphia, PA: Society for Industrial and Applied Mathematics; 1992. ISBN: 0-89871-274-2.
  19. Kingsbury N. Complex wavelets for shift invariant analysis and filtering of signals. Appl Comput Harmon Anal 2001; 10(3): 234-253. DOI: 10.1006/acha.2000.0343.
  20. Vapnik VN, Chervonenkis AYa. Pattern recognition theory [In Russian]. Moscow: "Nauka" Publisher; 1974.
  21. Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H. Feature selection: A data perspective. ACM Comput Surv 2017; 50(6): 94. DOI: 10.1145/3136625.
  22. Baulin FB, Buryi EV. Feature selection technique and classification algorithm selection for the case of viewing aspect defined with known error [In Russian]. Communication Technologies and Nets 2018: 169-173.
  23. Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugen 1936; 7: 179-188. DOI: 10.1111/j.1469-1809.1936.tb02137.x.
  24. Juefei-Xu F, Savvides M. Multi-class Fukunaga Koontz discriminant analysis for enhanced face recognition. Patt Recogn 2016; 52(C): 186-205. DOI: 10.1016/j.patcog.2015.10.007.
  25. Shurygin AM. Applied stochastics: robustness, estimates and predictions [In Russian]. Moscow: "Financy i Statistica" Publisher, 2000. ISBN: 5-279-02201-2.
  26. Saito N. Simultaneous noise suppression and signal compression using a library of orthonormal bases and the minimum description length criterion. In Book: Foufoula-Georgiou E, Kumar P, eds. Wavelets in geophysics. Vol 4. Wavelet analysis and its applications. New York: Academic Press; 1994: 299-324. DOI: 10.1016/B978-0-08-052087-2.50017-7.
  27. Kankar PK, Sharma SC, Harsha SP. Fault diagnosis of ball bearings using continuous wavelet transform. Appl Soft Comput 2011; 11(2): 2300-2312. DOI: 10.1016/j.asoc.2010.08.011.
  28. Baulin FB, Buryi EV. Advantages of wavelet transform based feature extraction in range profile recognition by means of an artificial neural network. The 11th Int Conf "Acoustooptical and Radar Methods for Information Measurements and Processing" (ARMIMP-2018) 2018: 38-41.
  29. Stone M. Cross-validatory choice and assessment of statistical predictions. J R Stat Soc Series B Stat Methodol 1974; 36(2): 111-147. DOI: 10.1111/j.2517-6161.1974.tb00994.x.
  30. Fukunaga K. Introduction to statistical pattern recognition. 2nd ed. Boston: Academic Press; 1990. ISBN: 978-0-12-269851-4.
  31. Chinchor N. MUC-4 evaluation metrics. Proc 4th Conf on Message Understanding 1992: 22-29. DOI: 10.3115/1072064.1072067.
  32. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143(1): 29-36. DOI: 10.1148/radiology.143.1.7063747.
  33. Rijsbergen CJV. Information retrieval. 2nd ed. London, Boston: Butterworth-Heinemann; 1979. ISBN: 978-0-408-70929-3.

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