Calibration of full-waveform airborne laser scanning data for 3D object segmentation

Phd ThesisAirborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic object recognition and extraction processes. 3D object segmentation is considered as one of the major research topics in the field of laser scanning for feature recognition and object extraction applications. The demand for automatic segmentation has significantly increased with the emergence of full-waveform (FWF) ALS, which potentially offers an unlimited number of return echoes. FWF has shown potential to improve available segmentation and classification techniques through exploiting the additional physical observables which are provided alongside the standard geometric information. However, use of the FWF additional information is not recommended without prior radiometric calibration, taking into consideration all the parameters affecting the backscattered energy. The main focus of this research is to calibrate the additional information from FWF to develop the potential of point clouds for segmentation algorithms. Echo amplitude normalisation as a function of local incidence angle was identified as a particularly critical aspect, and a novel echo amplitude normalisation approach, termed the Robust Surface Normal (RSN) method, has been developed. Following the radar equation, a comprehensive radiometric calibration routine is introduced to account for all variables affecting the backscattered laser signal. Thereafter, a segmentation algorithm is developed, which utilises the raw 3D point clouds to estimate the normal for individual echoes based on the RSN method. The segmentation criterion is selected as the normal vector augmented by the calibrated backscatter signals. The developed segmentation routine aims to fully integrate FWF data to improve feature recognition and 3D object segmentation applications. The routine was tested over various feature types from two datasets with different properties to assess its potential. The results are compared to those delivered through utilizing only geometric information, without the additional FWF radiometric information, to assess performance over existing methods. The results approved the potential of the FWF additional observables to improve segmentation algorithms. The new approach was validated against manual segmentation results, revealing a successful automatic implementation and achieving an accuracy of 82%

Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data

This study presents and compares new methods to describe the 3D canopy structure with Airborne Laser Scanning (ALS) waveform data as well as ALS point data. The ALS waveform data were analyzed in three different ways; by summing the intensity of the waveforms in height intervals (a); by first normalizing the waveforms with an algorithm based on Beer-Lambert law to compensate for the shielding effect of higher vegetation layers on reflection from lower layers and then summing the intensity (b); and by deriving points from the waveforms (c). As a comparison, conventional, discrete return ALS point data from the laser scanning system were also analyzed (d). The study area was located in hemi-boreal, spruce dominated forest in the southwest of Sweden (Lat. 58° N, Long. 13° E). The vegetation volume profile was defined as the volume of all tree crowns and shrubs in 1 dm height intervals in a field plot and the total vegetation volume as the sum of the vegetation volume profile in the field plot. The total vegetation volume was estimated for 68 field plots with 12 m radius from the proportion between the amount of ALS reflections from the vegetation and the total amount of ALS reflections based on Beer-Lambert law. ALS profiles were derived from the distribution of the ALS data above the ground in 1 dm height intervals. The ALS profiles were rescaled using the estimated total vegetation volume to derive the amount of vegetation at different heights above the ground. The root mean square error (RMSE) for cross validated regression estimates of the total vegetation volume was 31.9% for ALS waveform data (a), 27.6% for normalized waveform data (b), 29.1% for point data derived from the ALS waveforms (c), and 36.5% for ALS point data from the laser scanning system (d). The correspondence between the estimated vegetation volume profiles was also best for the normalized waveform data and the point data derived from the ALS waveforms and worst for ALS point data from the laser scanning system as demonstrated by the Reynolds error index. The results suggest that ALS waveform data describe the volumetric aspects of vertical vegetation structure somewhat more accurately than ALS point data from the laser scanning system and that compensation for the shielding effect of higher vegetation layers is useful. The new methods for estimation of vegetation volume profiles from ALS data could be used in the future to derive 3D models of the vegetation structure in large areas

Moniajalliset aaltomuotolaserpiirteet metsäpuissa – fenologian, puulajien ja skannausgeometrian vaikutus

Ilmalaserkeilauksella ”airborne LiDAR” (Light Detection and Ranging) tuotetaan korkearesoluutioista 3D-tietoa erittäin kustannustehokkaasti. Tämänhetkiset metsien inventointimenetelmät yhdistävät sekä LiDARin että passiivisen ilmakuvauksen. Mahdollisuus pelkän LiDARin käyttöön on erittäin houkutteleva, koska se johtaisi ainakin osittain kustannusten alenemiseen. Tässä tutkimuksessa keskitytään ns. täyden aaltomuodon havaintoihin, mitkä sisältävät enemmän tietoa lähetetystä ja vastaanotetusta signaalista kuin ’tavanomaiset’ pistepilvet. Tässä tutkimuksessa tarkastellaan metsän latvuston rakenteellisten ominaisuuksien ja LiDAR-signaalien välisiä riippuvuuksia ja pyritään lisäämään ymmärrystämme LiDARin ja kasvillisuuden välisistä vuorovaikutuksista ja tekijöistä, jotka rajoittavat nykyistä kykyä käyttää LiDAR-dataa mm. puulajitulkintaan, ja sitä, kuinka erilaisin prosessointi ja laskentamenetelmin voimme parantaa LiDARin tulkintaa metsässä. Tämän tutkimuksen tarkoituksena on ymmärtää, kuinka erilaisia aaltomuotopiirteitä voidaan tulkita ja kuinka piirteet käyttäytyvät muuttuvan fenologian mukaan. Tutkimusaineisto koostuu kolmesta peräkkäisestä LiDAR- ja ilmakuva kampanjasta, jotka on tehty alueella 38 kuukauden aikana sekä tämän ajanjakson aikana mitatuista maastoreferenssipuista. Käytössä on monen ajankohdan dataa, mikä koostuu kolmesta toistetusta laserkeilauksesta, jotka kaikki käyttivät samaa sensoria, lentoratoja ja keilausasetuksia. Koska LiDAR-havainnot ovat vertailukelpoisia ja samoista puista, voidaan ns. "puutekijää" tutkia ja vaihtelua aaltomuodon ominaisuuksien välillä toistuvissa keilauksissa seurata. Fenologiset muutokset ovat havaittavissa, koska aineistot sisältävät talven (lehdetön aika), alkukesän (alhainen lehtialaindeksi (LAI) havupuilla) ja loppukesän (täyslehti, korkea LAI). Myös skannauszeniittikulman (SZA) vaikutus aaltomuodon ominaisuuksiin ja piirteisiin otettiin huomioon, koska sama puu voitiin nähdä usealta lentolinjalta. Tulokset osoittavat, että huolellisella koeasettelulla on mahdollista havaita lajien sisäisiä ja lajien välisiä fenologisia eroja ja muutoksia moniajallisista aaltomuotopiirteistä. SZA:lla ei ollut merkittävää vaikutusta tuloksiin. Puulajiluokitus onnistui hyvin vaihtelevissa fenologisissa olosuhteissa ja erirakenteellisissa metsiköissä. Fenologiset muutokset olivat hyvin ilmeisiä kausivihannoilla puilla, mutta melko pieniä ainavihannilla havupuilla. Kokonaistarkkuudet puulajiluokituksessa olivat talvella 92 %, alkukesällä 88 % ja loppukesällä 84 % kasvatusmetsässä ja talvella 84 %, alkukesällä 81 % ja loppukesällä 83 % vanhassa puustossa. "puutekijän" osoitettiin olevan merkittävä. Lajien sisäinen varianssi johtuu pääasiassa puutekijästä eli lajinsisäinen ominaisuusvarianssi edustaa luonnollista vaihtelua saman lajin puiden välillä.Airborne LiDAR (Light Detection And Ranging) produces high-resolution and cost-efficient 3D data. Currently, forest inventories combine the use of both LiDAR and passive imaging by cameras, and the possibility of using LiDAR only is very tempting as it would lead to cost reduction. Focus of this study is on the full-waveform observations that extent the information content compared to conventional point clouds and are somewhat rarer to have access to. This study explores basic dependencies between structural canopy features and LiDAR signals over time and aims at augmenting our understanding of LiDAR-vegetation interactions and factors limiting our current ability to use pulsed LiDAR data for species detection, and how possibilities to overcome those limitations. Motivation is to understand how different waveform features can be interpreted and how the features behave over time with changing vegetation phenology. The study material consists of three consecutive LiDAR campaigns and aerial imaging surveys done in the area during a 38-month period and field reference trees that have been measured during this period. I use multi-temporal data that comprise three repeated acquisitions, which all applied same sensor, trajectories, as well as sensor and acquisition settings. As I had repeated LiDAR observations of the same trees where the acquisition settings are comparable, I could study the so-called ‘tree effect’ and overall co-variation between waveform features in the repeated acquisitions. Phenological changes are available as the data comprises winter (leaf-off), early summer (low LAI in conifers) and late summer data (full leaf, high LAI). The influence of scan zenith angle (SZA) on waveform features and attributes is also considered, as the same tree can be seen from multiple strips. The results showed that by using careful experimentation it is possible to detect intra- and interspecies phenological changes from multitemporal full-waveform data, while SZA did not have markable effect on the WF features. I was also able to perform well with the tree species classification task in varying phenological conditions. The phenological changes were very apparent on deciduous trees, but rather small on evergreen conifers. In a 45-year-old stand, the overall accuracies in tree species classification were 92, 87 and 88 % for winter, early summer, and late summer, respectively. These figures were 84, 81, and 83 % for in an old growth forest. The ‘tree effect’ was shown to be significant, i.e., many of the WF features of trees were correlated over time. The intra-species feature variance that is due to the tree effect represents natural variation between trees of the same species

Analysis of full-waveform LiDAR data for classification of an orange orchard scene

Full-waveform laser scanning data acquired with a Riegl LMS-Q560 instrument were used to classify an orange orchard into orange trees, grass and ground using waveform parameters alone. Gaussian decomposition was performed on this data capture from the National Airborne Field Experiment in November 2006 using a custom peak-detection procedure and a trust-region-reflective algorithm for fitting Gauss functions. Calibration was carried out using waveforms returned from a road surface, and the backscattering coefficient c was derived for every waveform peak. The processed data were then analysed according to the number of returns detected within each waveform and classified into three classes based on pulse width and c. For single-peak waveforms the scatterplot of c versus pulse width was used to distinguish between ground, grass and orange trees. In the case of multiple returns, the relationship between first (or first plus middle) and last return c values was used to separate ground from other targets. Refinement of this classification, and further sub-classification into grass and orange trees was performed using the c versus pulse width scatterplots of last returns. In all cases the separation was carried out using a decision tree with empirical relationships between the waveform parameters. Ground points were successfully separated from orange tree points. The most difficult class to separate and verify was grass, but those points in general corresponded well with the grass areas identified in the aerial photography. The overall accuracy reached 91%, using photography and relative elevation as ground truth. The overall accuracy for two classes, orange tree and combined class of grass and ground, yielded 95%. Finally, the backscattering coefficient c of single-peak waveforms was also used to derive reflectance values of the three classes. The reflectance of the orange tree class (0.31) and ground class (0.60) are consistent with published values at the wavelength of the Riegl scanner (1550 nm). The grass class reflectance (0.46) falls in between the other two classes as might be expected, as this class has a mixture of the contributions of both vegetation and ground reflectance properties

Extracting More Data from LiDAR in Forested Areas by Analyzing Waveform Shape

Light Detection And Ranging (LiDAR) in forested areas is used for constructing Digital Terrain Models (DTMs), estimating biomass carbon and timber volume and estimating foliage distribution as an indicator of tree growth and health. All of these purposes are hindered by the inability to distinguish the source of returns as foliage, stems, understorey and the ground except by their relative positions. The ability to separate these returns would improve all analyses significantly. Furthermore, waveform metrics providing information on foliage density could improve forest health and growth estimates. In this study, the potential to use waveform LiDAR was investigated. Aerial waveform LiDAR data were acquired for a New Zealand radiata pine plantation forest, and Leaf Area Density (LAD) was measured in the field. Waveform peaks with a good signal-to-noise ratio were analyzed and each described with a Gaussian peak height, half-height width, and an exponential decay constant. All parameters varied substantially across all surface types, ruling out the potential to determine source characteristics for individual returns, particularly those with a lower signal-to-noise ratio. However, pulses on the ground on average had a greater intensity, decay constant and a narrower peak than returns from coniferous foliage. When spatially averaged, canopy foliage density (measured as LAD) varied significantly, and was found to be most highly correlated with the volume-average exponential decay rate. A simple model based on the Beer-Lambert law is proposed to explain this relationship, and proposes waveform decay rates as a new metric that is less affected by shadowing than intensity-based metrics. This correlation began to fail when peaks with poorer curve fits were included

ANALYSIS OF FULL-WAVEFORM LIDAR DATA FOR CLASSIFICATION OF URBAN AREAS

International audienceIn contrast to conventional airborne multi-echo laser scanner systems, full-waveform (FW) lidar systems are able to record the entire emitted and backscattered signal of each laser pulse. Instead of clouds of individual 3D points, FW devices provide connected 1D profiles of the 3D scene, which contain more detailed and additional information about the structure of the illuminated surfaces. This paper is focused on the analysis of FW data in urban areas. The problem of modelling FW lidar signals is first tackled. The standard method assumes the waveform to be the superposition of signal contributions of each scattering object in such a laser beam, which are approximated by Gaussian distributions. This model is suitable in many cases, especially in vegetated terrain. However, since it is not tailored to urban waveforms, the generalized Gaussian model is selected instead here. Then, a pattern recognition method for urban area classification is proposed. A supervised method using Support Vector Machines is performed on the FW point cloud based on the parameters extracted from the post-processing step. Results show that it is possible to partition urban areas in building, vegetation, natural ground and artificial ground regions with high accuracy using only lidar waveforms