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Analyzing the Impact of Different Filtering Methods on Satellite EPJ Web Conferences 237, 08005 (2020) https://doi.org/10.1051/epjconf/202023708005 ILRC 29 ANALYZING THE IMPACT OF DIFFERENT FILTERING METHODS ON SATELLITE ALTIMETRY FULL WAVEFORM DECOMPOSITION Zhijie Zhang1, 2, Huan Xie2，*, Xiaohua Tong2, Binbin Li2, Yunwen Li2, Hanwei Zhang1 1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China 2College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China *Email: [email protected] ABSTRACT Space-borne laser altimetry is a powerful remote sensing technology, which can provide high Filtering is an essential step in the denoising of resolution information on surface elevations and satellite altimetry full waveform data, since any ground cover to the Earth science community by deformation and distortion in the shape of the digitally recording the return laser pulse. NASA’s waveform can cause errors in range estimation and Ice, Cloud, and land Elevation Satellite further waveform decomposition will also be (ICESat/GLAS) was the first Earth-orbiting laser adversely affected. This paper evaluated altimeter mission that operated from 2003–2009[1], comprehensive performance of the popular its product data have been widely used in filtering approaches like Gaussian filter, Taubin monitoring glaciers[2], ice shelves[3], vegetation, filter, Wavelet filter, and EMD based filter by and land elevation[4], etc. Building on this heritage, simulated waveform data and ICESat/GLAS the next generation of space-borne Advanced waveform. Firstly, according to the principle of Topographic Laser Altimeter System (ICESat- each filter, the optimal parameters of filtering 2/ATLAS) is seeking to monitor a diverse range of algorithm by ergodic tests were selected, then the geophysical phenomena at unprecedented levels of Gaussian function using Levenberg-Marquardt precision and accuracy by photon-counting method was used to fit full waveform to exact technolgy[5]. Comparing with America, though waveform parameters (i.e. peak amplitude, China started later in this aspect, currently ZY3-02 position, and half-width). Thirdly, through as the first experimental altimeter acquired 44 comparing SNR, RMSE of the pre and post tracks effective data, and the combined adjustment filtering simulation waveform, and the consistency of its laser and image data can actually improve the ratio, the average error of peak amplitude, position, accuracy of uncontrolled elevation[6].. GaoFen7 and half-width in each Gaussian components of the (GF-7) is the first civil laser three-dimensional fitted simulation waveform, verified the mapping satellite in China, and will launch this effectiveness of these filters and analyzed their year whose main objective is to aid high precision influence on decomposition accuracy. Both the mapping. simulation experiments and ICESat/GLAS experimental results suggested that the Taubin Critical to the success of full waveform satellites is filter had superior performance with the lowest the ability to interpret the shape of the digitally- peak position error, which turns out it has recorded return laser pulse and extract accurate advantage in full waveform denoising and time for specific reflecting surfaces within the contributes to better full waveform decomposition. footprint (e.g., first and last), and thus to geolocate However, it introduces more parameters needed to the desired reflecting surface directly. Brenner be selected. The self-adaptive EMD based proposed Gaussian function to decompose the approach has the highest consistency, which shows waveforms[7]. Specifically, from the real waveform EMD-based method is more suitable for the signal, initial parameters need to be identified first denoising of satellite altimetry full waveform for the fitting step, more precisely the number of decomposition. peaks or modes together with width, amplitude and location for each mode. Due to the noisy nature of Key words: satellite altimetry, full waveform, many waveforms, estimation of initial values from denoising, filtering, decomposition the raw signal results in a large number of modes 1. INTRODUCTION with a low amplitude and a narrow width. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/). EPJ Web Conferences 237, 08005 (2020) https://doi.org/10.1051/epjconf/202023708005 ILRC 29 Therefore, it is necessary to smooth the waveforms ′ 푥 = 푥푖 + 휆 ∑푗휖푖∗ 푤푖푗(푥푗 − 푥푖) in order to avoid the noise-induced Gaussian { 푖 (1) ′′ ′ ′ ′ ′ components and help increase the fitting accuracy. 푥푖 = 푥푖 + 휇 ∑푗휖푖∗ 푤푖푗 (푥푗 − 푥푖 ) Many methods have been proposed for both noise In the equation, 푥푖 denotes the raw waveform data removal and improvement of the signal-to-noise ; ′ ratio (SNR) in full waveform decomposition. But before filtering 푥푖 means the waveform after some methods are not appropriate in all cases and the first smoothing; 푖∗ is the adjacent point set of need specific assumptions. Typically, in Low-Pass ′′ ， filters, the high frequency segments are assumed to i and j is the point of the set; 푥푖 stands for the contain noise. Wiener filter requires to pre-estimate waveform after the second smoothing; λ and μ a cross-correlation vector between input signal and mean the scale，and 휆 > 0，휇 < 0，휇 < expected signal, which is rather difficult to carry . ′ ′ out because the desired signal is unknown before −휆 푤푖푗(푤푖푗 ) denotes the weight of 푥푗(푥푗 ) filtering. To Gaussian filter, the selection of an ′ ， appropriate kernel width is a key but difficult work, impact on 푥푖(푥푖 ) which could adopt the and the denoising effect comparing with other filter reciprocal of point number in the set[11]. In the like Taubin filter, Wavelet filter and EMD filter paper, we chose 휆 equals 0.9057, 휇 equals -0.9072 lack explicit comparison. In this paper, we report by many experiments. our experience of using those different kinds of 2.3 Wavelet filter filters. In order to keep the results objective, the principle of the filters are first described and their While the wavelet filter is quite different from the smoothing parameters are investigated and above two methods, the basic idea of it is to start evaluated. And following the results of these with a basis wavelet function with regularity, methods applied to simulation and ICESat/GLAS locality and oscillation, and to get a function family data are presented and compared. At last, through scaling and translation transformations[12]. experimental results are discussed and concluding In this paper, we adopted“Daubechies” basis (db4) remarks are offered, with expectation to provide with the level 3, and the soft threshold method for reference for the upcoming GF-7 data process. estimating wavelet coefficients. 2. METHODOLOGY 2.4 EMD Filter Empirical mode decomposition (EMD), introduced 2.1 Gaussian Filter by Huang et al[13] is a method for decomposing Gaussian filtering is realized by convolution complex, multi-component signal into several between the Gaussian kernel function with the elementary Intrinsic Mode Functions (IMFs) as original signal[8]. The smoothing performance of it eq.(2) shows. Differing from Fourier and wavelet could be adjusted by Gaussian kernel width σ. The analysis which have predefined basis, EMD uses width of flat plane pulse is derived as the best only scale and frequency characters of the original Gaussian kernel width for a fixed width Gaussian signal. EMD is a local, fully data-driven and self- [9] filtering . In the paper, we chose width of GLAS adaptive analysis approach. In order to let its transmit pulse 6ns as the kernel width to determine instantaneous frequency have a meaningful the weight of each bin. When the bin length is 9, interpretation, the IMF has to satisfy two the smoothing effect is better through many tests. conditions: (1) in the whole data set, the number of 2.2 Taubin Filter extrema and the number of zero-crossings must On the basis of Gaussian filter, Taubin filter adds either equal or differ at most by one; (2) at any two scale factors 휇 and 휆 to adjust the point, the local average of upper and lower envelop [10] is zero. smoothing degree , which effectively avoid the 푛 defects of edge data shrink led by Gaussian filter. 푆0(푡) = ∑푖=푗+1 퐼푀퐹푖(푡) + 푟푛(푡) (2) It uses formula eq.(1) for iterative processing of Where 푆0(푡) means the smoothing signal after waveform data. deleting the first j IMFs that are considered as noise. In this paper, if the Hurst exponent of a certain IMF is less than 0.5, then it is regarded as noise. 2 EPJ Web Conferences 237, 08005 (2020) https://doi.org/10.1051/epjconf/202023708005 ILRC 29 3. RUSULTS AND DISCUSSIONS In this section, we used simulated waveform data and ICESat/GLAS waveform to test each filter performance. Due to the true Gaussian components are known in simulation waveform data, therefore, we compared average absolute error of peak value, peak position, half-width error between the fitted waveform and real waveform. While, the ICESat/GLAS data we just compared these waveform with same Gaussian number and their Fig.1 Denoising effect comparision of different filiters fitting accuracy. 3.1 Simulation tests and results In order to analyze fitting correct rate and fitting accuracy, a group of the full-waveform echoes 5000 in total were simulated using addition of Gaussian functions and Gaussian white noise. The number of Gaussian function from fitting result was compared with the number of Gaussian Fig.2 SNR gain and RMSE of different filiters function given in simulation signal to verify correctness of fitting result. If these two number are equal and the location error is less than 1ns, the fitting result is considered as consistency. The specific parameters are as follows: Table1. Parameters set of simulation full waveform signal SNR_set = [10, 15, 20, 25, 30, 35, 40] Amp_set = [0.1, 0.15, 0.2, 0.25, 0.3,0.
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