Abstract
In this article, we propose a light detection and ranging (LiDAR) data denoising scheme for wind profile observation as a part of quality control procedure for wind velocity monitoring and windshear detection. The proposed denoising scheme consists of several components. (i) It selects LiDAR observations according to their SNR values so that serious noisy data can be removed. (ii) A polar-based total variation smoothing term is employed to regularize LiDAR observations. (iii) The regularization parameters are automatically determined to balance the data-fitting term and the total variation smoothing term. Numerical results for LiDAR data collected at the Hong Kong International Airport are reported to demonstrate that the denoising performance of the proposed method is better than that of the testing LiDAR data denoising schemes in the literature.
Highlights
Light detection and ranging (LiDAR) technique [1, 2] is a remote sensing tool that plays a significant role in environmental monitoring sciences
By analysis of the LiDAR data collected at the Hong Kong International Airport, we propose a LiDAR data denoising method based on the minimization of an objective function containing (i) the data-fitting term between the observed LiDAR data and the denoised data; (ii) the polar-based total variation regularization term that is used to smooth LiDAR observations; and (iii) the weighting term of LiDAR observations that is employed to control whether LiDAR observations are used in the denoising procedure based on their signal-to-noise ratio (SNR) values and neighborhood observations
The LiDAR data we study in this paper are time-varying and distance-varying, so that we compare the results by the discrete wavelet transform (DWT) method [9] and the EMDCIIT method [16]. ey are denoted as “DWT” and “EMDCIIT.” we consider the method used by Baranov et al [18] and Newsom et al [19] as well as the method used by Hong Kong Observatory
Summary
Light detection and ranging (LiDAR) technique [1, 2] is a remote sensing tool that plays a significant role in environmental monitoring sciences. It is widely used in meteorological data observing. Due to the impact of measurement environments and some other reasons, there would be some observation errors and very noisy observations in the observational LiDAR data as the range of observation increases [4] It can have a serious effect in different LiDAR data applications such as windshear detection. It can have a serious effect in different LiDAR data applications such as windshear detection. erefore, it is indispensable to develop an effective denoising method as a part of quality control for LiDAR observational data to improve the data quality and remove bad observations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
