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«PREDICTION OF TOPOGRAPHIC AND BATHYMETRIC MEASUREMENT PERFORMANCE OF AIRBORNE LOW-SNR LIDAR SYSTEMS By TRISTAN COSSIO A DISSERTATION PRESENTED TO THE ...»

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PREDICTION OF TOPOGRAPHIC AND BATHYMETRIC MEASUREMENT

PERFORMANCE OF AIRBORNE LOW-SNR LIDAR SYSTEMS

By

TRISTAN COSSIO

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

© 2009 Tristan Cossio To my friends and family

ACKNOWLEDGMENTS

I thank my friends and family for helping me through this whole process. I also thank my professor Clint Slatton, for his patience and wisdom.

TABLE OF CONTENTS

page ACKNOWLEDGMENTS

LIST OF TABLES

LIST OF FIGURES

Abstract

CHAPTER 1 INTRODUCTION

Introduction to Laser Altimetry

Fundamental Concept of Operation

Lidar System Design

Limitations of the Traditional ALSM Approach

The Low SNR Approach to Laser Altimetry

Concept

Previous Applications

Motivation

2 SIMULATOR DESIGN AND IMPLEMENTATION FOR TOPOGRAPHIC

MEASUREMENT

Overview

Range Acquisition

Optical Scanner

Ground Point Estimation

Signal Strength Estimation

Noise Estimation

Pulse Distortion and Delay

Laser Phenomena

Speckle

Shot Noise

Scintillation

Construction of Range Gate

Data Output

3 TOPOGRAPHIC RESULTS AND ANALYSIS

Simulation Setup

Solar Background Noise

Signal-to-Noise Ratio

Signal Strength

Elevation Statistics

Sample Data from Preliminary CATS Testing

Discussion

4 SIMULATOR DESIGN AND IMPLEMENTATION FOR BATHYMETRIC MEASUREMENT

Ocean Environment Overview

Water Surface near the Coast

Sea Foam

Wave Influence

Integrated Surface Model

Backscatter from Water Column

Returns from Ocean Bottom

5 BATHYMETRIC RESULTS AND ANALYSIS

Simulation Setup

Depth Performance

Sea Foam Coverage

Bottom Reflectance

Sea Depth

Discussion

6 TARGET DETECTION ALGORITHM DESIGN AND IMPLEMENTATION

Overview

Spatial Correlation Feature

Concept

Parameter Selection

Distribution of Data

Topographic DTM Generation

CRR Background

Sliding CRR Concept

Parameter Selection

Results

Bathymetric DTM Generation

Results

Discrimination of Target Areas

Sliding CRR vs. SCF

Summary

7 QUANTITATIVE ASSESSMENT OF TARGET DETECTION PERFORMANCE........... 97

Overview

Topographic Results

Target Properties

Terrain Shape

System Specifications

Bathymetric Results

Water Clarity and Depth

System Specifications

Discussion

8 CONCLUSIONS

Research Scope

Simulator Overview

Simulation Results

Conclusions

APPENDIX CATS HARDWARE AND SOFTWARE

Introduction

Hardware Design

Optics Design and Implementation

Receiver Design and Implementation

Processing and Analysis Tools

Processing

Analysis

Field Testing

Scanner Motion and Processing

Channel Orientation

Range Accuracy

Detector Sensitivity

Water Penetration

Return Signal Stability

Beam Polarization

Outstanding Issues

LIST OF REFERENCES

BIOGRAPHICAL SKETCH

–  –  –

2-1 Effect of slope and roughness on temporal width of the return pulse.

3-1 Atmospheric parameters used for simulation.

3-2 System parameters used for simulation.

3-3 Mean and standard deviation of number of signal events detected per channel at nadir, for various surface reflectances.

3-4 Mean, median, and mode elevation (Z) values in meters, calculated for a virtual level surface at 1 m elevation.

5-1 IOPs for bathymetric simulations

5-2 Standard parameters for bathymetric simulations.

5-3 Estimated distribution parameters from simulated data over coastal water.

5-4 Mean number of signal events from the ocean bottom per beamlet as foam coverage is varied from 0% to 60%.

5-5 Mean number of signal events from the ocean bottom per beamlet as reflectance coefficient is varied from 0.05 to 0.50.

5-6 Mean number of signal events from the ocean bottom per beamlet as sea depth is varied from 1 m to 7 m.

6-1 Error statistics for topographic DTMs with surface reflectivity set to 0.15.

6-2 Error statistics for bathymetric DTMs with surface reflectivity set to 0.15.

7-1 Reflectivity characteristics of common construction materials, at a wavelength of 550 nm.





–  –  –

1-1 Examples of various scan patterns that can be generated on the ground by a dualwedge Risley prism scanner

1-2 Plot of efficiency versus receiver threshold (in photoelectrons)

1-3 CFD extraction of returns on a multi-modal waveform

2-1 Modular design of LSNR-lidar simulator showing transmitting, propagation, and receiving blocks

2-2 CATS footprint composition and overlap

2-3 The dimensions of the boundary box (blue dashed box) are determined using the maximum height of the terrain surface, z g, max, in order to restrict the size of the array of candidate intersection points

2-4 Calculation of the perpendicular distance between the candidate ground points and the laser pointing vector.

2-5 Determination of the interpolated ground point using intersection of the laser vector with a polygon defined by sample points S1, S 2, and S 3 on the virtual ground truth surface

2-6 Probability of registering at least one signal event, given a single photoelectron threshold, as a function of the expected signal strength in photoelectrons (p. e.)............. 41 2-7 Normalized standard deviation of signal intensity due to shot noise as a function of expected signal strength

2-8 Simulated range gate for a single CATS footprint (96 channels) over a flat level surface

Projection of the point cloud in Fig. 2-8 into a XYZ coordinate frame

2-9 3-1 Elevation histograms for 200 shots using CATS nominal parameters (96 beamlets per shot) and a ground elevation of 1.0 m

3-2 SNR plotted as a function of solar zenith angle

3-3 SNR plotted as a function of solar zenith angle

3-4 Standard deviation of elevation values in a 2 meter window centered about the true terrain

3-5 Standard deviation of elevation values in a 2 meter window centered about the true terrain

5-1 Elevation histograms for 200 simulated footprints over pure sea water

5-2 Elevation histograms for 200 simulated footprints over coastal ocean conditions............ 73 6-1 Illustration of the SCF as a neighborhood operation

6-2 Across-track displacement between adjacent footprints across the laser swath................ 90 6-3 Probability distribution function for beamlet footprints, estimated by Parzenwindowing simulated returns using a 2D Gaussian kernel

6-4 Histogram of weighted SCF

6-5 Illustration of the CRR concept

6-6 Typical bathymetric lidar return waveform

6-7 Estimated return waveform after application of 2D LPF to the footprint event histogram

6-8 Surface-based detection scheme

6-9 Surface truth for the simulated scene

6-10 Resulting point cloud from simulation of 1000 footprints in the scene depicted in Figure 6-9

6-11 Remaining data points after filtering by SCF value

6-12 DTM generated for the point cloud shown in Fig. 6-10

Overhead ( XY ) view of the classified point cloud

6-13 7-1 Representative surfaces for the random terrain shapes

7-2 Operating characteristic curves for CATS parameters over flat terrain

7-3 Operating characteristic curves for CATS parameters

7-4 Operating characteristic curves for selected laser PRF values

7-5 Operating characteristic curves for CATS parameters over pure sea water of 2 m depth

7-6 Operating characteristic curves for CATS parameters over coastal waters of 2 m depth

7-7 Operating characteristic curves for CATS parameters over coastal water of varying depth

7-8 Operating characteristic curves for selected laser PRF values, over 5 m of pure water.. 110 7-9 Operating characteristic curves for selected transmitted pulse energy values, over 5 m of coastal water

A-1 Schematic showing the conceptual design of the CATS sensor head.

A-2 Image of CATS hardware prototype.

A-3 Receiver block diagram for n channels

A-4 Binary data structure of the event history file output by CATS

A-5 Top-down view of scanner with telescoping mounting cover removed

A-6 Rotational period of one scanner optical wedge calculated for CATS field data (obtained August 2008)

A-7 Several errors corrupt the shot time tag entries in CATS data

A-8 (Top) Image of investigated building, 320 m away from sensor. (Bottom) Isometric view of reconstructed point cloud

A-9 (Top) Image of investigated building, 550 m away from sensor. (Bottom) Topdown view of reconstructed point cloud

A-10 CATS footprint centered on 12’ x 12’ painted wood target

A-11 Histogram of hits for each channel

A-12 With the central four beamlets positioned on a target of high reflectivity (white painted metal), channels 14, 15, 22, and 23 registered a high number of returns from the target surface

A-13 Measured building dimensions, using CATS (left) and ILRIS (right) point cloud data. 144 A-14 Number of recorded signal events, at three PMT voltage settings: 2300V (blue), 2400V (green), and 2500V (red)

A-15 Total number of atmospheric scatter events at three PMT voltage settings: 2300V (left), 2400V(center), and 2500V (right)

A-16 Target configuration for ground-based CATS water penetration evaluation

A-17 Pure water was used to test CATS penetration through waters of high clarity................ 146 A-18 Sea water retrieved from Cedar Key FL was used to test CATS penetration through waters of high turbidity

A-19 Signal strength over a 10 second data set, using a 10000 shot window to estimate the necessary statistics

A-20 CATS beamlets incident on white painted wood

A-21 Moving sum window of hits per shot, as a function of shot number

–  –  –

Chair: K. Clint Slatton Major: Electrical and Computer Engineering Low signal-to-noise ratio (LSNR) lidar (light detection and ranging) is an alternative paradigm to traditional lidar based on the detection of return signals at the single photoelectron level. The objective of this work was to predict low altitude (600 m) LSNR lidar system performance with regards to elevation measurement and target detection capability in topographic (dry land) and bathymetric (shallow water) scenarios.

A modular numerical sensor model has been developed to provide data for further analysis due to the dearth of operational low altitude LSNR lidar systems. This simulator tool is described in detail, with consideration given to atmospheric effects, surface conditions, and the effects of laser phenomenology. Measurement performance analysis of the simulated topographic data showed results comparable to commercially available lidar systems, with a standard deviation of less than 12 cm for calculated elevation values. Bathymetric results, although dependent largely on water turbidity, were indicative of meter-scale horizontal data spacing for sea depths less than 5 m.

The high prevalence of noise in LSNR lidar data introduces significant difficulties in data analysis. Novel algorithms to reduce noise are described, with particular focus on their integration into an end-to-end target detection classifier for both dry and submerged targets (cube blocks, 0.5 m to 1.0 m on a side). The key characteristic exploited to discriminate signal and noise is the temporal coherence of signal events versus the random distribution of noise events.

Target detection performance over dry earth was observed to be robust, reliably detecting over 90% of targets with a minimal false alarm rate. Comparable results were observed in waters of high clarity, where the investigated system was generally able to detect more than 70% of targets to a depth of 5 m.

The results of the study show that CATS, the University of Florida’s LSNR lidar prototype, is capable of high fidelity (decimeter-scale) coverage of the topographic zone with limited applicability to shallow waters less than 5 m deep. To increase the spatial-temporal contrast between signal and noise events, laser pulse rate is the optimal system characteristic to improve in future LSNR lidar units.

–  –  –

Laser ranging first emerged as a promising measurement technique in the early 1970s, as part of the Apollo Command and Service Module project [1]. Data collection from laser altimetry was attempted successfully in the Apollo 15, 16, and 17 missions.

In the three decades since, laser ranging has become a dominant technology in the high resolution measurement of topography. Laser altimeters on airborne and spaceborne platforms have proven capable of providing detailed mapping of a wide variety of surfaces, including lunar and earth topography, coastal water bathymetry, forest canopy structure, and ice sheet elevation [1],[2],[3],[4],[5].

Fundamental Concept of Operation Laser altimetry is an active remote sensing process similar to radar but instead using optical wavelengths (typically in the 400 – 1500 nm range), in the form of laser light, as the illuminating source. A range measurement is acquired through the precise measurement of the two-way propagation time of the emitted laser pulse. Within this general paradigm, there is flexibility in system structure and device characteristics. System details are selected based on the desired mission.

Lidar System Design



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