LIDAR WAVEFORM MODELING USING A MARKED POINT PROCESS

International audienceLidar waveforms are 1D signal consisting of a train of echoes where each of them correspond to a scattering target of the Earth surface. Modeling these echoes with the appropriate parametric function is necessary to retrieve physical information about these objects and characterize their properties. This paper presents a marked point process based model to reconstruct a lidar signal in terms of a set of parametric functions. The model takes into account both a data term which measures the coherence between the models and the waveforms, and a regularizing term which introduces physical knowledge on the reconstructed signal. We search for the best configuration of functions by performing a Reversible Jump Markov Chain Monte Carlo sampler coupled with a simulated annealing. Results are finally presented on different kinds of signals in urban areas

POINT CLOUD GENERATION FROM GAUSSIAN DECOMPOSITION OF THE WAVEFORM LASER SIGNAL WITH GENETIC ALGORITHMS

Recent developments in LIDAR technology lead to the availability of the waveform systems, which capture and digitize the whole return of the emitted LASER pulse. As many objects may cause multiple returns in the same echo, one task is to detect and separate different echoes within the same digitized measurement. In this paper the results of a study aimed at LASER signal waveform decomposition using genetic algorithms are introduced. The proposed method is based on the Gaussian decomposition approach and analyzes each digitized return to compute one or more points. Initially, the number of peaks contained in the waveform is determined by a simple peak detection method, with a local maximum point algorithm. When more than one peak is detected, genetic algorithms are applied to estimate the amplitude, time and standard deviation of each peak within the digitized signal. With this methodology it was possible to increase the number of points by approximately 17 % compared to the point cloud obtained using commercial software. The best results were obtained in areas with high vegetation, and thus the methodology can be applied to the generation of denser points cloud in forest area

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

A Marked Point Process for Modeling Lidar Waveforms

International audienceLidar waveforms are 1-D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based upon a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a reversible jump Markov chain Monte Carlo (RJMCMC) sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported

Geração de nuvem de pontos por decomposição gaussiana da full-waveform do sinal laser com algoritmos genéticos e classificação Fuzzy

Orientador: Jorge Antonio Silva CentenoTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências da Terra, Programa de Pós-Graduação em Ciências Geodésicas. Defesa : Curitiba, 23/02/2018.Inclui referências: p. 130-134Resumo: Os sistemas convencionais de mapeamento topográfico com a tecnologia LIDAR registram até cinco retornos na forma discreta. Avanços tecnológicos ocorridos nas últimas décadas proporcionaram o desenvolvimento de sistemas LIDAR full-waveform que digitalizam completamente o sinal de retorno. Instalado em aeronave sobrevoando a 1.000 metros de altura, o feixe LASER emitido por estes sistemas gera footprint pequeno, com diâmetro variando entre 0,2 m e 0,8 m. Quando o feixe intercepta parcialmente diferentes alvos, múltiplos retornos podem ser determinados dependendo da metodologia utilizada para a detecção dos picos na waveform. Estes picos contêm informações sobre as características físicas das superfícies iluminadas, tais como a refletividade, rugosidade, altura e inclinação. Pesquisas têm sido realizadas para a detecção dos picos empregando diferentes técnicas, tais como a decomposição, deconvolução e correlação entre os sinais emitido e recebido. Nesta tese foi desenvolvida uma metodologia para a decomposição Gaussiana das waveforms com Algoritmos Genéticos e classificação dos pontos com abordagem Fuzzy. Os dados foram medidos sobre área urbana com edificações, estrada de asfalto, solo exposto, vegetação arbórea e rasteira. Com os Algoritmos Genéticos foi realizado o processo de busca e otimização dos parâmetros de amplitude, desvio padrão e tempo, cujos valores iniciais foram estimados em algoritmo de simples detecção de picos com ponto de máximo local. As distâncias dos alvos foram calculadas utilizando o tempo e, no processo de georreferenciamento a partir dos parâmetros de emissão do feixe LASER, determinaram-se as coordenadas dos pontos no sistema geodésico de referência. Foram realizados dois experimentos aplicando a metodologia de decomposição Gaussiana das waveforms e em ambos houve aumento na quantidade de pontos, respectivamente de 16,9 % e 18,1 %, em comparação com as nuvens de pontos geradas por software comercial, os melhores resultados ocorreram em áreas de vegetação arbórea. A classificação dos pontos foi realizada com Lógica Fuzzy utilizando os parâmetros de amplitude e desvio padrão, a inferência foi implementada com o método Mamdani e a desnebulização ocorreu pelo método do centroide, nos dois experimentos as acurácias globais foram de 86,0 % e 86,7 %, comprovando a viabilidade da metodologia desenvolvida. Palavras-chave: Lidar Full-Waveform, Decomposição Gaussiana, Algoritmos Genéticos, Lógica Fuzzy.Abstract: Conventional topographic LIDAR systems record up to five returns in the discrete-form. Technological advances in the last decades have impelled the development of full-waveform LIDAR systems that digitize the whole return signal of the emitted LASER pulse. These systems installed in aircraft flying at 1,000 meters high generate small footprints with a diameter ranging from 0.2 m to 0.8 m. When the LASER beam partially illuminate different targets, multiple returns can be determined depending on the methodology used for the peaks detection in the waveform. These peaks contain information about the physical characteristics of the illuminated surfaces, such as reflectivity, roughness, height and slope. Several researches have been carried out to detect the peaks using different techniques such as decomposition, deconvolution and correlation between the emitted and received signals. In this doctoral thesis was developed a methodology for the Gaussian decomposition of the waveform with Genetic Algorithms and points classification using Fuzzy approach. The waveforms data were measured on an urban area with buildings, asphalt road, exposed soil, arboreal and undergrowth vegetation. The Genetic Algorithms were used in the search and optimization process of Gaussian parameters, the initial values were estimated in a simple peak detection algorithm with the maximum local point method. For each detected peak the amplitude, standard deviation and time parameters were determined. The targets distances were calculated using the time and, in the georeferencing procedure using the LASER beam emission parameters, the coordinates of the points were determined in the geodetic reference system. Two experiments were performed applying the Gaussian decomposition of the waveforms methodology and there was an increase in the number of points, respectively of 16.9% and 18.1%, compared to the points cloud generated by commercial software, the best results were obtained in areas with arboreal vegetation. The points classification was performed with Fuzzy Logic using the parameters of amplitude and standard deviation, the inference was implemented with the Mamdani method and the defuzzification with the centroid method, in the two experiments were obtained overall accuracy of 86.0% and 86.7%, proving the viability of the methodology developed. Keywords: Lidar Full-Waveform, Gaussian Decomposition, Genetic Algorithms, Fuzzy Logic

The Assessment of habitat condition and consevation status of lowland British woodlands using earth observation techniques.

The successful implementation of habitat preservation and management demands regular and spatially explicit monitoring of conservation status at a range of scales based on indicators. Woodland condition can be described in terms of compositional and structural attributes (e.g. overstorey, understorey, ground flora), evidence of natural turnover (e.g. deadwood and tree regeneration), andanthropogenic influences (e.g.disturbance, damage). Woodland condition assessments are currently conducted via fieldwork, which is hampered by cost, spatial coverage, objectiveness and repeatability.This projectevaluates the ability of airborne remote sensing (RS) techniques to assess woodland condition, utilising a sensor-fusion approach to survey a foreststudy site and develop condition indicators. Here condition is based on measures of structural and compositional diversity in the woodland vertical profile, with consideration of the presence of native species, deadwood, and tree regeneration. A 22 km2 study area was established in the New Forest, Hampshire, UK, which contained a variety of forest types, including managed plantation, semi-ancient coniferous and deciduous woodland. Fieldwork was conducted in 41 field plots located across this range of forest types, each with varying properties. The field plots were 30x30m in size and recorded a total of 39 forest metrics relating to individual elements of condition as identified in the literature. Airborne hyperspectral data (visible and near-infrared) and small footprint LiDAR capturing both discrete-return (DR) and full-waveform (FW) data were acquired simultaneously, under both leaf-on and leaf-off conditions in 2010. For the combined leaf-on and leaf-off datasets a total of 154 metrics were extracted from the hyperspectral data, 187 metrics from the DR LiDAR and 252 metrics from the FW LiDAR. This comprised both area-based and individual tree crown metrics. These metrics were entered into two statistical approaches, ordinary least squares and Akaike information criterion regression, in order to estimate each of the 39 field plot-level forest variables. These estimated variables were then used as inputs to six forest condition assessment approaches identified in the literature. In total, 35 of the 39 field plot-level forest variables could be estimated with a validated NRMSE value below 0.4 using RS data (23 of these models had NRMSE values below 0.3). Over half of these models involved the use of FW LiDAR data on its own or combined with hyperspectral data, demonstrating this to be single most able dataset. Due to the synoptic coverage of the RS data, each of these field plot variables could be estimated and mapped continuously over the entire study site at the 30x30m resolution (i.e. field plot-level scale). The RS estimated field variables were then used as inputs to six forest condition assessment approaches identified in the literature.Three of the derived condition indices were successful based on correspondence with field validation data and woodlandcompartment boundaries. The three successful condition assessment methods were driven primarily by tree size and tree size variation. The best technique for assessing woodland condition was a score-based method which combined seventeen inputs which relate to tree species composition, tree size and variability, deadwood, and understory components; all of whichwere shown to be derived successfully from the appropriate combination of airborne hyperspectral and LiDAR datasets. The approach demonstrated in this project therefore shows that conventional methods of assessing forest condition can be applied with RS derived inputs for woodland assessment purposes over landscape-scale areas