Waveform LiDAR signal denoising based on connected domains

Frontiers of Optoelectronics - Tập 10 - Trang 388-394 - 2017
Liyu Sun1, Zhiwei Dong1, Ruihuan Zhang1, Rongwei Fan1, Deying Chen1
1National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology, Harbin, China

Tóm tắt

The streak tube imaging light detection and ranging (LiDAR) is a new type of waveform sampling laser imaging radar whose echo signals are stripe images with a high frame rate. In this study, the morphological and statistical characteristics of stripe signals are analyzed in detail. Based on the concept of mathematical morphology denoising, connected domains are constructed in a noisecontaining stripe image, and the noise is removed using the difference in connected domains area between signals and noises. It is shown that, for stripe signals, the proposed denoising method is significantly more efficient than Wiener filtering.

Tài liệu tham khảo

Li Q, Wang Y, Wang Q, Li Z. Noise suppression algorithm of coherent ladar range image. Acta Optica Sinica, 2005, 25(05): 581–584 Li Q, Wang Q, Li Z, Li L, Jiang L. Image processing on laser imaging radar. Chinese Journal of Lasers, 2002, A29(09): 826–828 Gleckler A D, Gelbart A, Bowden J M. Multispectral and hyperspectral 3D imaging Lidar based upon the multipleslit streak tube imaging lidar. Proceedings of the Society for Photo-Instrumentation Engineers, 2001, 4377: 328–335 Gleckler A D, Gelbart A. Three-dimensional imaging polarimetry. Proceedings of the Society for Photo-Instrumentation Engineers, 2001, 4377: 175–185 Nevis A J. Automated processing for streak tube imaging lidar data. Proceedings of the Society for Photo-Instrumentation Engineers, 2003, 5089: 119–129 Gelbart A, Redman B C, Light R S, Schwartzlow C A, Griffis A J. Flash lidar based on multiple-slit streak tube imaging lidar. Proceedings of the Society for Photo-Instrumentation Engineers, 2002, 4723: 9–18 Sun J F, Liu D, Ge M D, Wang Q. Image pre-processing algorithm of underwater target for streak tube imaging lidar. Chinese Journal of Lasers, 2013, 40(07): 211–214 Sheng Y P, Sun J F, Xu D W. Application analysis of short-range ocean surface monitoring for streak tube imaging lidar. Electro-Optic Technology Application, 2012, (1): 34–36,70 Sun J F, Gao J, Wei J S, Wang Q. Research development of underwater detection imaging based on streak tube imaging lidar. Infrared and Laser Engineering, 2010, 39(05): 811–814 Li S N, Liu J B, Guang Y H, Zang J H, Wang Q. Maximum acquisition range calculation for multi-wavelength streak tube image lidar. Acta Photonica Sinica, 2007, 36(S1): 106–109 Wei J S, Wang Q, Sun J F, Gao J. Experiment of four-dimensional imaging with single-slit streak tube lidar. Chinese Journal of Lasers, 2010, 37(5): 1231–1235 Zhang J H, Li S N, Wang Q, Liu J B. Noise analyzing and processing of streak image for streak tube imaging lidar. Acta Photonica Sinica, 2008, 37(8): 1533–1538 Dong Z W, Zhang R H, Zhang W B. Noise features in streak tube lidar echo signal. Acta Optica Sinica, 2016, 36(09): 296–300 Dong Z W, Zhang W B, Fan R W. Streak tube principle lidar imaging simulation and experiment Infrared and Laser Engineering. Infrared and Laser Engineering, 2016, 45(07): 100–104 Gleckler A. Streak tube imaging lidar for electro-optic identification. In: Proceedings of 4th International Symposium on Technology and the Mine Problem, 2001 Redman B C, Griffis A J, Schibley E B. Streak tube imaging lidar (STIL) for 3-D imaging of terrestrial targets. In: Proceedings of the MSS Specialty Group on Active E-O Systems, 2000 Bian X D. Research on stripe image processing for threedimensional laser mapping. Dissertation for the Master Degree. Harbin: Harbin Institute of Technology, 2015, 20–21 Lim J S. Two-Dimensional Signal and Image Processing. Englewood Cliffs, NJ: Prentice Hall, 1990 Fan J M. Design and application of the labeling algorithm of 8-adjacent connecting area for massive gray scale images. Dissertation for the Master Degree. Kaifeng: Henan University, 2015 Suzuki K, Horiba I, Sugie N. Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding, 2003, 89(1): 1–23