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, III/2, V/3: WAVEFORM LIDAR FOR REMOTE SENSING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: EXPLORING WEAK AND OVERLAPPED RETURNS OF A LIDAR WAVEFORM WITH A WAVELET-BASED ECHO DETECTOR C. K. Wang

Document type:: Multivolume work

Structure type:: Chapter

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 how far two overlapped echoes separated will influence the detection results. 2. WAVELET-BASED ECHO DETECTOR The wavelet transform is a tool to decompose a signal in terms of elementary contributions over dilated and translated wavelets. One of the continues wavelet transform(CWT) applications is resolving overlapped peaks in a signal(Jiao et al., 2008). The CWT at time uw and scale s can be represented as wf ws) =(fw..)=| fOw, Od (D where /{t) 1s the input signal, * denotes the complex conjugate, y, .(r) 1s the wavelet function controlled by a scale factor s and a translation factor uw. Wf{u, s) is the so called wavelet coefficients. Applying CWT to the waveforms can be considered as measuring the similarity between the waveform and the wavelets. If the chosen mother wavelet and the responded echo are similar in shape, then the locations where WC peaks occur imply the positions of the response echo in the waveform. Figure 2 shows an example of detecting echoes using the wavelet-based detector. A signal(waveform) is applied wavelet transform at two scales. Consequently we can obtain the WC (Wf (u, s4), Wf (u, s2)) at each scale. Taking the result at the smaller scale s; firstly, one can see that the locations where the WC peaks (Wf(u4,s41), Wf (u2, 43), Wf (us, $4) ) occur corresponding to the positions of echoes in the waveform. However for the case of larger scale s,, only two echoes are detected. This can be explained that the expanded wavelet cannot "see" the echoes whose size 1s smaller than the wavelet itself. signal wavelet (scale 1) OON REOR M OW NOM dbi m eomm mos ub oo xot XE ue $e oim m de de wow oul mom os xp mom ww wd an wees Hmmm SiO LB 21 HF s ; Vries, S MG s Winn) NE rd f IAN AAAS SAA ® E E 9 = = æ æ æ = signal 2 % wavelet 2 NUN (scale 2) 2 \ x » m i n FETT Wits) Hb, Sytem i "ers A Tr Tus, Lr v Figure 2. the interpretation of detecting echoes by CWT. Since the wavelet can be scaled by a scale factor, the wavelet- based detector is able to deal with different system with variant echo width, for example, the echo width is 5 ns for Leica ALS60 system and 8 ns for Optech ALTM 3100. Therefore to 530 optimize the detection results, an appropriate mother wavelet and a scale factor need to be prior determined. Many researches have considered the responded echo as a Gaussian function. For this reason the Gaussian wavelet is chosen as the mother wavelet in our study. Additionally by exploring some waveform samples, the scale parameter can be determined according to detection results. 3. EXPERIMENT AND RESULTS 3.1 Waveform simulation A received waveform is a power function of time and can be expressed as follows (Wagner et al., 2006; Mallet et al., 2010): P(0- Y 5*0 @) where S(t) is the system waveform of the laser scanner, o;(t) is the apparent cross-section, N is the number of echoes and k; is a value varied by range between sensor and target. Eq. (2) indicates that a return echo is the convolution of system waveform and the apparent cross-section of a scatter. Wagner et al.(2006) have reported that the system waveform of Riegl LMS-Q560 can be well described by a Gaussian model. If the apparent cross-section of a scatter is assumed to be of Gaussian function, then the convolution of two Gaussian curves gives again a Gaussian distribution. The received waveform P.(t) can be rewritten as: ny N = P (t) = > Pe 255i (3) i=l where t; is the round-trip time, s, ; the standard deviation of the echo pulse, and P; the amplitude of cluster i. For this reason, the Gaussian function is chosen to simulate the return echo. The simulated waveform can be represented as follows: m w() - 3 g,() n 4) 2 G1) 25° (5) | where w 1s the simulated waveform, m the number of return echoes, n the noises which have a normal distribution(Unoise» Onoise), 4 the location of time domain, and s the echo width which can be represented by full width at half maximum (FWHM =2+/2In2s). Figure 3 shows an example of a simulated waveform. g(t) 2 A-exp -

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