Skip to main content
SpringerLink
Log in

A waveform decomposition technique based on wavelet function and differential cuckoo search algorithm

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Waveform decomposition is widely used for the separation of echoes from full-waveform LiDAR (FWL) signal, and some previous studies employed Gaussian function for laser pulse modeling and waveform decomposition. However, it was difficult to guarantee the waveform parameters on the neighbor of optimal solution, because of the limited amplitude range. In addition, waveform parameters were usually set by the amplitude and location of inflection points, which may enlarge the difference between decomposed and original waveforms. Hence, a novel waveform decomposition technique based on wavelet function and differential cuckoo search algorithm is proposed, where wavelet function has a high-order vanishing moment, cuckoo search algorithm has a strong optimization ability, and differential operator avoids trapping into the local optima. The proposed technique is tested on airborne FWL point cloud and compared with other corresponding approaches, experimental results demonstrate that the decomposed waveforms are obtained with a reasonable convergence rate and feature characterization, as the rRMSE is lower than 7% for all of waveforms, the whole process of waveform decomposition only takes 0.3s, and waveform parameters are used as the features to recognize different objects from point cloud.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  • Abdelgayed TS, Morsi WG, Sidhu TS (2017) A new approach for fault classification in microgrids using optimal wavelet functions matching pursuit. IEEE Trans Smart Grid 9:4838–4846

    Article  Google Scholar 

  • Aubry A, Carotenuto V, De Maio A (2016) Forcing multiple spectral compatibility constraints in radar waveforms. IEEE Signal Process Lett 23:483–487

    Article  Google Scholar 

  • Buterin S (2018) On an inverse spectral problem for first-order integro-differential operators with discontinuities. Appl Math Lett 78:65–71

    Article  MathSciNet  Google Scholar 

  • Chitara D, Niazi KR, Swarnkar A et al (2018) Cuckoo search optimization algorithm for designing of a multimachine power system stabilizer. IEEE Trans Ind Appl 54:3056–3065

    Article  Google Scholar 

  • Civicioglu P, Besdok E, Gunen MA et al (2018) Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms. Neural Comput Appl 645:1–15

    Google Scholar 

  • Cohen MX (2019) A better way to define and describe Morlet wavelets for time-frequency analysis. NeuroImage 199:81–86

    Article  Google Scholar 

  • Lee CY, Yao X (2004) Evolutionary programming using mutations based on the Lévy probability distribution. IEEE Trans Evol Comput 8:1–13

    Article  Google Scholar 

  • Li D, Xu L, Li X (2018) Full-waveform LiDAR echo decomposition based on wavelet decomposition and particle swarm optimization. Meas Sci Technol 28:045205

    Article  Google Scholar 

  • Li D, Xu L, Xie X et al (2018) Co-path full-waveform LiDAR for detection of multiple along-path objects. Opt Lasers Eng 111:211–221

    Article  Google Scholar 

  • Li H, Li G, Cai Z et al (2019) Full-waveform LiDAR echo decomposition method. J Remote Sens 23:89–98

    Google Scholar 

  • Liu S, Hu Y, Li C et al (2017) Machinery condition prediction based on wavelet and support vector machine. J Intell Manuf 28:1045–1055

    Article  Google Scholar 

  • Mallet C, Bretar F (2009) Full-waveform topographic LiDAR: State-of-the-art. ISPRS J Photogramm Remote Sens 64:1–16

    Article  Google Scholar 

  • Mareli M, Twala B (2018) An adaptive cuckoo search algorithm for optimisation. Appl Comput Inform 14:107–115

    Article  Google Scholar 

  • Mellal MA, Williams EJ (2016) Parameter optimization of advanced machining processes using cuckoo optimization algorithm and hoopoe heuristic. J Intell Manuf 27:927–942

    Article  Google Scholar 

  • Milenkovic M, Wagner W, Quast R et al (2017) Total canopy transmittance estimated from small-footprint, full-waveform airborne LiDAR. ISPRS J Photogramm Remote Sens 128:61–72

    Article  Google Scholar 

  • Morin D, Planelis M, Guyett D et al (2018) Estimation of forest parameters combining multisensor high resolution remote sensing data. In: IEEE international geoscience and remote sensing symposium, pp 8801–8804

  • Mountrakis G, Li Y (2017) A linearly approximated iterative gaussian decomposition method for waveform LiDAR processing. ISPRS J Photogramm Remote Sens 129:200–211

    Article  Google Scholar 

  • Oliveira AA, Centeno JA, Hainosz FS (2008) Point cloud generation from Gaussian decomposition of the waveform laser signal with genetic algorithms. Bol Cienc Geodesicas 24:270–287

    Google Scholar 

  • Qin Y, Vu TT, Ban Y (2011) Toward an optimal algorithm for LiDAR waveform decomposition. IEEE Geosci Remote Sens Lett 9:482–486

    Article  Google Scholar 

  • Sadybekov MA, Imanbaev NS (2017) A regular differential operator with perturbed boundary condition. Math Notes 101:878–887

    Article  MathSciNet  Google Scholar 

  • Schafer R (2011) What is a Savitzky–Golay filter. IEEE Signal Process Mag 28:111–117

    Article  Google Scholar 

  • Shen X, Li Q, Wu G et al (2017) Decomposition of LiDAR waveforms by B-spline-based modeling. ISPRS J Photogramm Remote Sens 128:182–191

    Article  Google Scholar 

  • Shen J, Shang J, Sun J et al (2018) Waveform decomposition of echoes for airborne LiDAR based on seeker optimization algorithm. Chin J Lasers 45:1110004

    Article  Google Scholar 

  • Song S, Wang B, Gong W et al (2019) A new waveform decomposition method for multispectral LiDAR. ISPRS J Photogramm Remote Sens 149:40–49

    Article  Google Scholar 

  • Wang Z, Liu Z, Deng Z et al (2018) Phase extraction of non-stationary interference signal in frequency scanning interferometry using complex shifted Morlet wavelets. Opt Commun 420:26–33

    Article  Google Scholar 

  • Weitkamp C (2006) LiDAR: range-resolved optical remote sensing of the atmosphere. Springer, New York

    Google Scholar 

  • Witt C (2009) Rigorous runtime analysis of swarm intelligence algorithms-an overview. In: Swarm intelligence for multi-objective problems in data mining, pp 157–177

  • Wu Z, Alkhalifah T (2016) The optimized gradient method for full waveform inversion and its spectral implementation. Geophys J Int 205:1823–1831

    Article  Google Scholar 

  • Wu JM, Tsai MH, Huang YZ et al (2018) Applying an ensemble convolutional neural network with Savitzky–Golay filter to construct a phonocardiogram prediction model. Appl Soft Comput 29:29–40

    Google Scholar 

  • Xu F, Li F, Wang Y (2016) Modified Levenberg–Marquardt-based optimization method for LiDAR waveform decomposition. IEEE Geosci Remote Sens Lett 13:530–534

    Article  Google Scholar 

  • Yan W, Shaker A, El-Ashmawy N (2015) Urban land cover classification using airborne LiDAR data: a review. Remote Sens Environ 158:295–310

    Article  Google Scholar 

  • Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: World congress on nature & biologically inspired computing (NaBIC), vol 2009, pp 210–214

  • Yang B, Miao J, Fan Z et al (2018) Modified cuckoo search algorithm for the optimal placement of actuators problem. Appl Soft Comput 67:48–60

    Article  Google Scholar 

  • Yin T, Qi J, Gastellu-Etchegorry JP et al (2018) Gaussian decomposition of LiDAR waveform data simulated by dart. In: IEEE international geoscience and remote sensing symposium, pp 4300–4303

  • Zheng M (2015) Research of airborne LiDAR full-waveform data decomposition and point-cloud classification. Master’s thesis, Wuhan University

  • Zhou T, Popescu SC, Krause K et al (2017) Gold—a novel deconvolution algorithm with optimization for waveform LiDAR processing. ISPRS J Photogramm Remote Sens 129:131–150

    Article  Google Scholar 

Download references

Funding

This work was funded by the National Natural Science Foundation of China under Grant Nos. 41901296, 41925007, 41801394, the Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation under Grant No. 2018NGCM06, and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) under Grant No. 2642019046.

Author information

Authors and Affiliations

  1. Institute of Geological Survey, China University of Geosciences, Wuhan, 430074, People’s Republic of China

    Mingwei Wang & Shuai Xiong

  2. Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation, Wuhan, 430079, People’s Republic of China

    Mingwei Wang

  3. School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, People’s Republic of China

    Maolin Chen

  4. College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou, 450045, People’s Republic of China

    Peipei He

Authors
  1. Mingwei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuai Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maolin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peipei He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mingwei Wang.

Ethics declarations

Conflict of interest

Mingwei Wang and Shuai Xiong declare that they have no conflict of interest. Maolin Chen declares that he has no conflict of interest. Peipei He declares that she has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, M., Xiong, S., Chen, M. et al. A waveform decomposition technique based on wavelet function and differential cuckoo search algorithm. Soft Comput 25, 5909–5923 (2021). https://doi.org/10.1007/s00500-021-05583-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-05583-x

Keywords

Search

Navigation