Recovery of forest canopy parameters by inversion of multispectral LiDAR data

We describe the use of Bayesian inference techniques, notably Markov chain Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) methods, to recover forest structural and biochemical parameters from multispectral LiDAR (Light Detection and Ranging) data. We use a variable dimension, multi-layered model to represent a forest canopy or tree, and discuss the recovery of structure and depth profiles that relate to photochemical properties. We first demonstrate how simple vegetation indices such as the Normalized Differential Vegetation Index (NDVI), which relates to canopy biomass and light absorption, and Photochemical Reflectance Index (PRI) which is a measure of vegetation light use efficiency, can be measured from multispectral data. We further describe and demonstrate our layered approach on single wavelength real data, and on simulated multispectral data derived from real, rather than simulated, data sets. This evaluation shows successful recovery of a subset of parameters, as the complete recovery problem is ill-posed with the available data. We conclude that the approach has promise, and suggest future developments to address the current difficulties in parameter inversion

Bayesian methods for inverse problems with point clouds : applications to single-photon lidar

Single-photon light detection and ranging (lidar) has emerged as a prime candidate technology for depth imaging through challenging environments. This modality relies on constructing, for each pixel, a histogram of time delays between emitted light pulses and detected photon arrivals. The problem of estimating the number of imaged surfaces, their reflectivity and position becomes very challenging in the low-photon regime (which equates to short acquisition times) or relatively high background levels (i.e., strong ambient illumination). In a general setting, a variable number of surfaces can be observed per imaged pixel. The majority of existing methods assume exactly one surface per pixel, simplifying the reconstruction problem so that standard image processing techniques can be easily applied. However, this assumption hinders practical three-dimensional (3D) imaging applications, being restricted to controlled indoor scenarios. Moreover, other existing methods that relax this assumption achieve worse reconstructions, suffering from long execution times and large memory requirements. This thesis presents novel approaches to 3D reconstruction from single-photon lidar data, which are capable of identifying multiple surfaces in each pixel. The resulting algorithms obtain new state-of-the-art reconstructions without strong assumptions about the sensed scene. The models proposed here differ from standard image processing tools, being designed to capture correlations of manifold-like structures. Until now, a major limitation has been the significant amount of time required for the analysis of the recorded data. By combining statistical models with highly scalable computational tools from the computer graphics community, we demonstrate 3D reconstruction of complex outdoor scenes with processing times of the order of 20 ms, where the lidar data was acquired in broad daylight from distances up to 320 m. This has enabled robust, real-time target reconstruction of complex moving scenes, paving the way for single-photon lidar at video rates for practical 3D imaging applications

Single Tree Detection from Airborne Laser Scanning Data: A Stochastic Approach

Characterizing and monitoring forests are of great scientific and managerial interests, such as understanding the global carbon circle, biodiversity conservation and management of natural resources. As an alternative or compliment to traditional remote sensing techniques, airborne laser scanning (ALS) has been placed in a very advantageous position in forest studies, for its unique ability to directly measure the distribution of vegetation materials in the vertical direction, as well as the terrain beneath the forest canopy. Serving as basis for tree-wise forest biophysical parameter and species information retrieval, single tree detection is a very motivating research topic in forest inventory. The objective of the study is to develop a method from the perspective of computer vision to detect single trees automatically from ALS data. For this purpose, this study explored different aspects of the problem. It starts from an improved pipeline for canopy height model (CHM) generation, which alleviates the distortion of tree crown shapes presented on CHMs resulted from conventional procedures due to the shadow effects of ALS data and produces pit-free CHM. The single tree detection method consists of a hybrid framework which integrates low-level image processing techniques, i.e. local maxima filtering (LM) and marker-controlled watershed segmentation (MCWS), into a high-level probabilistic model. In the proposed approach, tree crowns in the forest plot are modelled as a configuration of circular objects. The configuration containing the best possible set of detected tree objects is estimated by a global optimization solver in a probabilistic framework. The model features an accelerated optimization process compared with classical stochastic models, e.g. marked point processes. The parameter estimation is another issue: the study investigated both a reference-based supervised and an Expectation-Maximization (EM) based unsupervised method to estimate the parameters in the model. The model was tested in a temperate mature coniferous forest in Ontario, Canada, as well as simulated coniferous forest plots with various degrees of crown overlap. The experimental results showed the effectiveness of our proposed method, which was capable of reducing the commission errors produced by local maxima filtering based methods, thus increasing the overall detection accuracy by approximately 10% on all of the datasets

High-Level Facade Image Interpretation using Marked Point Processes

In this thesis, we address facade image interpretation as one essential ingredient for the generation of high-detailed, semantic meaningful, three-dimensional city-models. Given a single rectified facade image, we detect relevant facade objects such as windows, entrances, and balconies, which yield a description of the image in terms of accurate position and size of these objects. Urban digital three-dimensional reconstruction and documentation is an active area of research with several potential applications, e.g., in the area of digital mapping for navigation, urban planning, emergency management, disaster control or the entertainment industry. A detailed building model which is not just a geometric object enriched with texture, allows for semantic requests as the number of floors or the location of balconies and entrances. Facade image interpretation is one essential step in order to yield such models. In this thesis, we propose the interpretation of facade images by combining evidence for the occurrence of individual object classes which we derive from data, and prior knowledge which guides the image interpretation in its entirety. We present a three-step procedure which generates features that are suited to describe relevant objects, learns a representation that is suited for object detection, and that enables the image interpretation using the results of object detection while incorporating prior knowledge about typical configurations of facade objects, which we learn from training data. According to these three sub-tasks, our major achievements are: We propose a novel method for facade image interpretation based on a marked point process. Therefor, we develop a model for the description of typical configurations of facade objects and propose an image interpretation system which combines evidence derived from data and prior knowledge about typical configurations of facade objects. In order to generate evidence from data, we propose a feature type which we call shapelets. They are scale invariant and provide large distinctiveness for facade objects. Segments of lines, arcs, and ellipses serve as basic features for the generation of shapelets. Therefor, we propose a novel line simplification approach which approximates given pixel-chains by a sequence of lines, circular, and elliptical arcs. Among others, it is based on an adaption to Douglas-Peucker's algorithm, which is based on circles as basic geometric elements We evaluate each step separately. We show the effects of polyline segmentation and simplification on several images with comparable good or even better results, referring to a state-of-the-art algorithm, which proves their large distinctiveness for facade objects. Using shapelets we provide a reasonable classification performance on a challenging dataset, including intra-class variations, clutter, and scale changes. Finally, we show promising results for the facade interpretation system on several datasets and provide a qualitative evaluation which demonstrates the capability of complete and accurate detection of facade objectsHigh-Level Interpretation von Fassaden-Bildern unter Benutzung von Markierten PunktprozessenDas Thema dieser Arbeit ist die Interpretation von Fassadenbildern als wesentlicher Beitrag zur Erstellung hoch detaillierter, semantisch reichhaltiger dreidimensionaler Stadtmodelle. In rektifizierten Einzelaufnahmen von Fassaden detektieren wir relevante Objekte wie Fenster, Türen und Balkone, um daraus eine Bildinterpretation in Form von präzisen Positionen und Größen dieser Objekte abzuleiten. Die digitale dreidimensionale Rekonstruktion urbaner Regionen ist ein aktives Forschungsfeld mit zahlreichen Anwendungen, beispielsweise der Herstellung digitaler Kartenwerke für Navigation, Stadtplanung, Notfallmanagement, Katastrophenschutz oder die Unterhaltungsindustrie. Detaillierte Gebäudemodelle, die nicht nur als geometrische Objekte repräsentiert und durch eine geeignete Textur visuell ansprechend dargestellt werden, erlauben semantische Anfragen, wie beispielsweise nach der Anzahl der Geschosse oder der Position der Balkone oder Eingänge. Die semantische Interpretation von Fassadenbildern ist ein wesentlicher Schritt für die Erzeugung solcher Modelle. In der vorliegenden Arbeit lösen wir diese Aufgabe, indem wir aus Daten abgeleitete Evidenz für das Vorkommen einzelner Objekte mit Vorwissen kombinieren, das die Analyse der gesamten Bildinterpretation steuert. Wir präsentieren dafür ein dreistufiges Verfahren: Wir erzeugen Bildmerkmale, die für die Beschreibung der relevanten Objekte geeignet sind. Wir lernen, auf Basis abgeleiteter Merkmale, eine Repräsentation dieser Objekte. Schließlich realisieren wir die Bildinterpretation basierend auf der zuvor gelernten Repräsentation und dem Vorwissen über typische Konfigurationen von Fassadenobjekten, welches wir aus Trainingsdaten ableiten. Wir leisten dazu die folgenden wissenschaftlichen Beiträge: Wir schlagen eine neuartige Me-thode zur Interpretation von Fassadenbildern vor, die einen sogenannten markierten Punktprozess verwendet. Dafür entwickeln wir ein Modell zur Beschreibung typischer Konfigurationen von Fassadenobjekten und entwickeln ein Bildinterpretationssystem, welches aus Daten abgeleitete Evidenz und a priori Wissen über typische Fassadenkonfigurationen kombiniert. Für die Erzeugung der Evidenz stellen wir Merkmale vor, die wir Shapelets nennen und die skaleninvariant und durch eine ausgesprochene Distinktivität im Bezug auf Fassadenobjekte gekennzeichnet sind. Als Basismerkmale für die Erzeugung der Shapelets dienen Linien-, Kreis- und Ellipsensegmente. Dafür stellen wir eine neuartige Methode zur Vereinfachung von Liniensegmenten vor, die eine Pixelkette durch eine Sequenz von geraden Linienstücken und elliptischen Bogensegmenten approximiert. Diese basiert unter anderem auf einer Adaption des Douglas-Peucker Algorithmus, die anstelle gerader Linienstücke, Bogensegmente als geometrische Basiselemente verwendet. Wir evaluieren jeden dieser drei Teilschritte separat. Wir zeigen Ergebnisse der Liniensegmen-tierung anhand verschiedener Bilder und weisen dabei vergleichbare und teilweise verbesserte Ergebnisse im Vergleich zu bestehende Verfahren nach. Für die vorgeschlagenen Shapelets weisen wir in der Evaluation ihre diskriminativen Eigenschaften im Bezug auf Fassadenobjekte nach. Wir erzeugen auf einem anspruchsvollen Datensatz von skalenvariablen Fassadenobjekten, mit starker Variabilität der Erscheinung innerhalb der Klassen, vielversprechende Klassifikationsergebnisse, die die Verwendbarkeit der gelernten Shapelets für die weitere Interpretation belegen. Schließlich zeigen wir Ergebnisse der Interpretation der Fassadenstruktur anhand verschiedener Datensätze. Die qualitative Evaluation demonstriert die Fähigkeit des vorgeschlagenen Lösungsansatzes zur vollständigen und präzisen Detektion der genannten Fassadenobjekte

9th Annual Jackson School of Geosciences Student Research Symposium, February 15, 2020

ConocoPhillipsGeological Science

Advanced photon counting techniques for long-range depth imaging

The Time-Correlated Single-Photon Counting (TCSPC) technique has emerged as a candidate approach for Light Detection and Ranging (LiDAR) and active depth imaging applications. The work of this Thesis concentrates on the development and investigation of functional TCSPC-based long-range scanning time-of-flight (TOF) depth imaging systems. Although these systems have several different configurations and functions, all can facilitate depth profiling of remote targets at low light levels and with good surface-to-surface depth resolution. Firstly, a Superconducting Nanowire Single-Photon Detector (SNSPD) and an InGaAs/InP Single-Photon Avalanche Diode (SPAD) module were employed for developing kilometre-range TOF depth imaging systems at wavelengths of ~1550 nm. Secondly, a TOF depth imaging system at a wavelength of 817 nm that incorporated a Complementary Metal-Oxide-Semiconductor (CMOS) 32×32 Si-SPAD detector array was developed. This system was used with structured illumination to examine the potential for covert, eye-safe and high-speed depth imaging. In order to improve the light coupling efficiency onto the detectors, the arrayed CMOS Si-SPAD detector chips were integrated with microlens arrays using flip-chip bonding technology. This approach led to the improvement in the fill factor by up to a factor of 15. Thirdly, a multispectral TCSPC-based full-waveform LiDAR system was developed using a tunable broadband pulsed supercontinuum laser source which can provide simultaneous multispectral illumination, at wavelengths of 531, 570, 670 and ~780 nm. The investigated multispectral reflectance data on a tree was used to provide the determination of physiological parameters as a function of the tree depth profile relating to biomass and foliage photosynthetic efficiency. Fourthly, depth images were estimated using spatial correlation techniques in order to reduce the aggregate number of photon required for depth reconstruction with low error. A depth imaging system was characterised and re-configured to reduce the effects of scintillation due to atmospheric turbulence. In addition, depth images were analysed in terms of spatial and depth resolution

Holistic interpretation of visual data based on topology:semantic segmentation of architectural facades

The work presented in this dissertation is a step towards effectively incorporating contextual knowledge in the task of semantic segmentation. To date, the use of context has been confined to the genre of the scene with a few exceptions in the field. Research has been directed towards enhancing appearance descriptors. While this is unarguably important, recent studies show that computer vision has reached a near-human level of performance in relying on these descriptors when objects have stable distinctive surface properties and in proper imaging conditions. When these conditions are not met, humans exploit their knowledge about the intrinsic geometric layout of the scene to make local decisions. Computer vision lags behind when it comes to this asset. For this reason, we aim to bridge the gap by presenting algorithms for semantic segmentation of building facades making use of scene topological aspects. We provide a classification scheme to carry out segmentation and recognition simultaneously.The algorithm is able to solve a single optimization function and yield a semantic interpretation of facades, relying on the modeling power of probabilistic graphs and efficient discrete combinatorial optimization tools. We tackle the same problem of semantic facade segmentation with the neural network approach.We attain accuracy figures that are on-par with the state-of-the-art in a fully automated pipeline.Starting from pixelwise classifications obtained via Convolutional Neural Networks (CNN). These are then structurally validated through a cascade of Restricted Boltzmann Machines (RBM) and Multi-Layer Perceptron (MLP) that regenerates the most likely layout. In the domain of architectural modeling, there is geometric multi-model fitting. We introduce a novel guided sampling algorithm based on Minimum Spanning Trees (MST), which surpasses other propagation techniques in terms of robustness to noise. We make a number of additional contributions such as measure of model deviation which captures variations among fitted models

7th Annual Jackson School of Geosciences Student Research Symposium, February 3, 2018

ConocoPhillipsGeological Science

Incorporating sedimentological observations, hydrogeophysics and conceptual knowledge to constrain 3D numerical heterogeneity models of alluvial systems

Coarse, braided river deposits are highly heterogeneous in terms of hydraulic properties and make up many groundwater reservoirs worldwide and more than two-thirds of the exploited aquifers in Switzerland. The management of these resources often requires the understanding of the subsurface flow processes and therefore, of the subsurface heterogeneity. While coarse, braided river deposits were the focus of many studies, the relationship between the braided river dynamics and the resulting sedimentary structures is still poorly known. A better knowledge of this relationship is the key to geologically more realistic modelling of the subsurface heterogeneity by accounting for the former controls on the fluvial system (slope of the floodplain, terraces, lateral confinement, bedrock steps, etc.). This thesis aims to (i) investigate the link between braided river morphodynamics and subsurface heterogeneity in a hydrogeological context and (ii) develop a numerical model of subsurface heterogeneity that accounts for this link. The sedimentological knowledge on coarse, braided river deposits was mainly gained from the observation of analogue coarse deposits in Switzerland. The Tagliamento River (northeast Italy) was chosen as a field laboratory to thoroughly study the geomorphology (surface) and near-surface sedimentology of coarse, braided rivers. The Tagliamento River is considered to be one of the last large semi-natural rivers of the Alps that has retained much of its natural sediment and discharge dynamics. The observations focused on a single reach sharing similar characteristics with partly confined valleys such as in the alpine foreland. The sedimentary structures of coarse, braided river deposits can be comprehensively described by a small limited number of sedimentary structures that have specific hydraulic properties. A handful of depositional elements were identified. Among them, the cross bedded trough fills can significantly influence the flow field because their highly-permeable cross beds act as fast flow conduit. Such trough fills most probably form from confluence scour holes. The geomorphological analysis of the Cimano-Pinzano reach is based on a LiDAR-derived digital elevation model (LiDAR-derived DEM), aerial and satellite photographs, a water-stage time series and regular field observations. Complex aggradation/degradation dynamics resulted in the formation of higher-lying zones incised by a drainage gully network and surrounded by zones that are often reworked by the river. The main geomorphological elements are identified in terms of their topographic signature and genesis, setting apart the trichotomy water–vegetation–bar. Two morphologies mark the active zones: a low-discharge morphology (low-discharge incisions and channels, slip-face lobes, etc.) superimposed on a high-discharge morphology (gravel sheets, scours, etc.). Based on the observations of vertical exposure of coarse, braided river deposits, each morphological element is associated with a depositional element. The preservation potential of the depositional elements is evaluated as a function of the river-bed aggradation dynamics and the resulting subsurface heterogeneity is discussed in terms of its impact on the subsurface flow. Ground-penetrating radar (GPR) surveys performed on the active zones of the Cimano-Pinzano reach imaged many cross bedded trough fills as observed in ancient deposits. Finding a link between these structures and the evolution of the morphology is challenging. Nevertheless, some hypotheses about the formation of the trough structures are advanced. All the observations suggest the importance of the gravel sheets in the formation of cross bedded trough fills. An object-based model was developed that mimics the dominant processes of floodplain dynamics. Contrary to existing models, this object-based model possesses the following properties: (i) it is consistent with field observations (outcrops, ground-penetrating radar data, etc.), (ii) it allows different sedimentological dynamics to be modeled that result in different subsurface heterogeneity patterns, and (iii) it is light in memory and computationally fast. To demonstrate its applicability, the object-based model is conditioned to interpreted two-dimensional data and the uncertainty on the three-dimensional subsurface heterogeneity is quantified with Monte Carlo sampling. The impact of an isolated trough fill complex on subsurface flow mixing is evaluated in terms of advective mixing. The trough fill complex is modelled with the object-based model that is fitted to GPR data. Hydraulic properties are assigned to the model cells and a subsurface flow through the model is simulated. The advective mixing is quantified with particle tracking. The results indicate strong advective mixing as well as a large flow deviation induced by the asymmetry of the trough fills with regard to the main flow direction. These findings depict possible advective mixing found in natural environments and can guide the interpretation of ecological processes such as in the hyporheic zone. The geomorphological and sedimentological characterisations of the coarse, braided Cimano-Pinzano reach of the Tagliamento River contribute to a better understanding of the morphodynamics of coarse, braided river in relation to the subsurface heterogeneity. The object-based model allows the simulation of various geological settings and the methodology developed for the stereological study can be adapted to other types of data without many changes. Preliminary results of subsurface flow simulations through coarse, braided river deposits show a strong subsurface flow mixing