XXII ISPRS Congress 2012: Technical Commission VII

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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:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

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

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

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:: [VII/7: THEORY AND EXPERIMENTS IN RADAR AND LIDAR]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: SIGNAL NOISE REDUCTION BASED ON WAVELET TRANSFORM IN TWO-WAVELENGTH LIDAR SYSTEM Shuo Shi, Wei Gong, Lilei Lv, Bo Zhu, Shalei Song

Document type:: Multivolume work

Structure type:: Chapter

MI | eT EC s onm am s NIC Optical Receiver ES oq ref Figure 1. The block diagram of two-wavelength lidar The two-wavelength lidar system works as follows. The laser emitter transmits the laser bean separately in red and near- infrared range. The laser light will be reflected to detect objects through the holophote composed of mirrors M1 and M2. Then backscatter signals can be received by the Schmidt-Cassegrain telescope with 200mm diameters, and be divided into two wavelengths through dichroic filters D1. Subsequently, PMTI and PMT2 are used to transform optical to electrical as the devices of photo-electric detection. Finally data acquisition and processing sub-system in the computer can obtain the back- scatter intensities of the objects. 2.2 Noise analysis After photo-electric detection, backscatter signals can be acquired by the data acquisition sub-system of two-wavelength lidar. There are two different signals respectively in red channel and near infrared channel as shown in Figure 2. near infrared |—— m in siis "m as xe Tp Eg Figure 2. Backscatter signals of two-wavelength lidar It can be seen from the Figure 2 that the backscatter signals received by the system are so bad that we cannot use directly for further calculation. The possible noise and sources should be analyzed before removal of noise. Dark current, background radiation noise and circuit noise caused by the detector and external environmental factors can seriously affect the backscatter signals. The noise from the sources is unrelated to each other in statistics. In the limited bandwidth of the signal, the noise could be regarded as white noise approximately and subject to normal distribution. Besides, there will also be some colored noise due to the remaining multimode interference effect of the laser beam. However, the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia generated thermal noise and shot noise can be neglected compared with dark current, background radiation noise and circuit noise. Thus, noise of backscatter signals in two- wavelength lidar system can be removed as the Gaussian white noise 3. METHOD BASED ON WAVELET TRANSFORM 3.1 Principle of wavelet transform Wavelet transform could map the signal to a group of basic functions which were generated by the wavelet expansion and shifting from. Thus the reasonable separation of signal could be made both in different frequency bands and different moments in the fact. Wavelet transform provides an effective tool for non- stationary description of the dynamic signals and the extraction of weak signal. 3.2 De-noising process Traditionally, the de-nosing process based on wavelet transform mainly consists of three steps: firstly, wavelet decomposition of the leakage signals, for which we must select the best mother wavelet and scale; secondly, threshold quantization of decomposing coefficients was made; finally, reconstruction wavelet de-noising signals was carried out. Especially, based on the characteristics of the backscatter signals, some of de-noising process in the two-wavelength lidar system should be discussed in detail. 3.2.1 Wavelet decomposition The wavelet transform is used to perform a multiscale decomposition on the noisy signals. More specifically, the corresponding wavelet coefficients are got by choosing suitable wavelet and wavelet decomposition layers. Daubechies wavelet function can effectively avoid the signal phase shifter and can also achieve better smoothing effect in the process of reconstruction on the basic of its symmetry and regularity property. As a result, three orders Daubeachies wavelet was chosen as the basic wavelet function in the de-noising process. Besides, the noisy signal was decomposed to 4 layers. 3.2.2 Removal of the singular value Through the analysis of characteristics of the backscatter signals, there are many singular values caused by varieties of noise sources in the signals. What's worse, the de-nosing result will be seriously affected if the singular values can not be removed. It is very convenient to distinguish the parts of noise and saltation of signal by 3o-rule applied in electronic measurement (HE Shi-biao, et al, 2002). The probability of the absolute value greater than three standard deviations is only about 3%, and all the values which are greater than 3o can be regarded as the coarse errors. In this way, singular values could be removed effectively. 3.2.3 Threshold quantification In order to extract the wavelet coefficients of signals and remove the wavelet coefficients of noise in the scales, a threshold should be chosen to carry out the quantification from the first level to the N layer of high frequency coefficients. In the real applications, there are many methods to solve the problem.

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