XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

Bibliographic data

Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VIII/3: Atmosphere, Climate and Weather]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: ATMOSPHERIC LIDAR NOISE REDUCTION BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION Jun LI, Wei GONG, Yingying Ma

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia ATMOSPHERIC LIDAR NOISE REDUCTION BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION Jun LI**, Wei GONG*, Yingying Ma“ ? LIESMARS, Wuhan University, Wuhan 430079, China - larkiner@gmail.com Commission VIII/3 KEY WORDS: LIDAR; Analysis; Algorithms; Atmosphere; Detection ABSTRACT: As an active remote sensing instrument, lidar provides a high spatial resolution vertical profile of aerosol optical properties. But the effective range and data reliability are often limited by various noises. Performing a proper denoising method will improve the quality of the signals obtained. The denoising method based on ensemble empirical mode decomposition (EEMD) is introduced, but the denoised results are difficult to evaluated. A dual field-of-view lidar for observing atmospheric aerosols is described. The backscattering signals obtained from two channels have different signal-to-noise ratios (SNR). To overcome the drawback of the simulation experiment, the performance of noise reduction can be investigated by comparing the high SNR signal and the denoised low SNR signal. With this approach, some parameters of the denoising method based on EEMD can be determined effectively. The experimental results show that the EEMD-based method with proper parameters can effectively increase the atmospheric lidar observing ability. 1. INTRODUCTION 1.1 General Instructions Aerosol can directly affect climate change by scattering and absorption of solar and other radiation, and also indirectly affect the radiation by affecting cloud formation. As an active remote sensing instrument, lidar provides a high spatial resolution vertical profile of aerosol optical properties!!! But the effective range and data reliability are often limited by various noises. Unfortunately, the lidar data inversion is sensitive to the lidar data at a far distance, which are under low signal-to-noise ratio conditions. Performing a proper denoising method will improve the quality of the signals obtained. The measured lidar signal contains the laser backscattering signal from aerosol and various noises. It can be expressed simply as Vased (1) 7 V(r) * N,G)- N,() (1) where Vneasured (r) = signal actually measured V(r) = signal from aerosol backscattering Ni(r) = noise due to background light Ne(r) = noise due to dark current and read out electronics. N» and N, can be statistically estimated by the signal obtained from a very far distance where the laser backscattering signal 1s negligible. * Corresponding author. Jun LI, larkiner@gmail.com The power of the received signal typically falls with an increase in range, but noise is usually considered as Gaussian white noise, which is stable with range. The signal-to-noise ratio (SNR) falls as the range increases, and the solution for the lidar equation becomes unstable and even fails because of the negative value produced by noise. So the signal must be denoised before data retrieval for the aerosol properties. There are several signal analysis methods widely adopted for the noise reduction in the lidar signal. Most lidar systems employ the multiple pulses averaging to enhance SNR. This method can be considered as a low pass filtering process at the cost of temporal resolution, high frequency backscattering signal is also smoothed. Wavelet analysis is developed rapidly as an effective tool for noise reduction”. A main drawback of the wavelet analysis is that the basis functions are fixed, and no such a basis function is proposed to correspond with the features of lidar signals currently. The selection of the best basis function is also a hard work. 2. DENOISING METHOD 2.1 Empirical mode decomposition Huang et al. introduced the empirical mode decomposition (EMD) for analyzing signals from non-stationary and non- linear processes in 1998. The EMD method is proved to address completeness, orthogonality, locality, and adaptivity which are necessary to describe non-stationary and non-linear processes. The major advantage of the EMD is posteriori adaptive, because the basis functions are derived from the

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