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:: FULL WAVEFORM LIDAR EXPLOITATION TECHNIQUE AND ITS EVALUATION IN THE MIXED FOREST HILLY REGION S. Chhatkuli, K. Mano, T. Kogure, K. Tachibana, H. Shimamura

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 Data acquisition was performed by using Leica's ALS60 sensor with WDM6S, mounted onboard a Cessna aircraft. WDM65 records the complete waveform of the reflection from the surface intercepting the laser footprint. The full waveform data was collected as 256 samples (à) 1ns. Field of View (FOV) of the scanner was set at 28 degrees, scan rate was set at 33.7 Hz and laser pulse rate used was 54000 Hz. The data acquisition was conducted by flying the aircraft at an altitude of 2491 m from the mean sea level. The terrain height of the area varied from 439 m to 790 m. The average point density of the transmitted pulse was 1point/sq.m. A computer program was developed to generate georeferenced point data from the full waveform return pulses. The point cloud generation algorithm implemented in our approach is explained below with an illustration of a typical case. (1) Discrete return pulse (black square dot) and the corresponding waveform data for the laser pulse are depicted in Figure 2 (top). The blue dots in the top image in Figure 2 are full waveform data plotted against time. As seen in the figure, the full waveform data are generally embedded with noise generated from the variety of sources. Hence, a 12 order Finite- duration Impulse Response (FIR) zero-phase digital filter technique was applied to smooth the waveform and to reduce the noise for the further analysis. For a 12 order FIR fiiter (Figure 3), the output is a weighted sum of the current and a finite number of previous values of the input as described in equation 1. y(n) 2 x(n)x h(0) - x( n -1)x h(1) ---- x(n-12)x h(12) (1) The red curve in Figure 2 (top) is the smooth waveform data obtained after the processing. The smooth waveform obtained from this technique preserves peak position, pulse width and skew of the laser reflection (Wong and Antoniou, 1994). (2) After the waveform smoothening, the next step is to distinguish the noise from the actual return intensity. For that purpose, we selected a threshold and assumed all the reflection below the given threshold as noise and excluded those values for further processing. This is a very straight forward approach and computationally very efficient. Two threshold values of intensity 16 and 17 were tested. When the threshold was set at 16, in some cases the points below ground level under the tree canopies were also generated. However with threshold 17, the last return peak of the waveform corresponds very well with the ground position level, under the tree canopies. Hence for the surveyed region, all the waveform return intensity below 17 was assumed noise and considered their intensity to be 0. (3) In the third step, the number of peaks and the location of the peaks in the smooth waveform data were, at first, estimated by finding the local maxima in the smooth waveform. Then an algorithm based on the Expectation Maximization (EM) (Persson et.al., 2005) was applied to correct the positions and number of the peaks by Gaussian decomposition. The crosshair symbols at the peak of the Gaussian bumps in Figure 2 (top) are the corrected peak positions estimated by the EM algorithm. (4) Finally, the correctly estimated peak positions were georeferenced to create a point cloud data. 506 The bottom image in Figure 2 shows the discrete LIDAR pulses (green dots), obtained during September, overlaid on a Triangulated Irregular Network (TIN) of a bare ground constructed from the discrete LiDAR pulses obtained during December. The red dots in the image are the position data obtained by georeferencing the Gaussian peaks of the smooth waveform data, depicted by the cross hair symbols in the Figure 2 (top image). From the figure it is clear that, for the particular laser pulse, the discrete return could record only one reflection from the top canopy layer however the proposed algorithm could detect 3 reflections of which the first one corresponds to the same discrete return and the last one corresponds to the ground level. Intensity 145163" 185 205 225 Time (ns) E Smooth Curve : Waveform data X GaussianPeak ® Discrete | Figure 2. Top: Discrete return pulse (black square dot), raw full waveform data (blue dots), smoothened waveform (red curve) and Gaussian peaks (crosshair symbols). Bottom: Discrete return pulses (green dots), full waveform return pulses (red dots) constructed from the Gaussian peaks depicted in the top image

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