Snapshot Three-Dimensional Surface Imaging With Multispectral Fringe Projection Profilometry

Fringe Projection Profilometry (FPP) is a popular method for non-contact optical surface measurements, including motion tracking. The technique derives 3D surface maps from phase maps estimated from the distortions of fringe patterns projected onto the surface of an object. Estimation of phase maps is commonly performed with spatial phase retrieval algorithms that use a series of complex data processing stages. Researchers must have advanced data analysis skills to process FPP data due to a lack of availability of simple research-oriented software tools. Chapter 2 describes a comprehensive FPP software tool called PhaseWareTM that allows novice to experienced users to perform pre-processing of fringe patterns, phase retrieval, phase unwrapping, and finally post-processing. The sequential process of acquiring fringe patterns from an object is necessary to sample the surface densely enough to accurately estimate surface profiles. Sequential fringe acquisition performs poorly if the object is in motion between fringe projections. To overcome this limitation, we developed a novel method of FPP called multispectral fringe projection profilometry (MFPP), where multiple fringe patterns are composited into a multispectral illumination pattern and a single multispectral camera is used to capture the frame. Chapter 3 introduces this new technique and shows how it can be used to perform 3D profilometry at video frame rates. Although the first attempt at MFPP significantly improved acquisition speed, it did not fully satisfy the condition for temporal phase retrieval, which requires at least three phase-shifted fringe patterns to characterize a surface. To overcome this limitation, Chapter 4 introduces an enhanced version of MFPP that utilized a specially designed multispectral illuminator to simultaneously project four p/2 phase-shifted fringe patterns onto an object. Combined with spectrally matched multispectral imaging, the refined MFPP method resulted in complete data for temporal phase retrieval using only a single camera exposure, thereby maintaining the high sampling speed for profilometry of moving objects. In conclusion, MFPP overcomes the limitations of sequential sampling imposed by FPP with temporal phase extraction without sacrificing data quality or accuracy of the reconstructed surface profiles. Since MFPP utilizes no moving parts and is based on MEMS technology, it is applicable to miniaturization for use in mobile devices and may be useful for space-constrained applications such as robotic surgery. Fringe Projection Profilometry (FPP) is a popular method for non-contact optical surface measurements such as motion tracking. The technique derives 3D surface maps from phase maps estimated from the distortions of fringe patterns projected onto the surface of the object. To estimate surface profiles accurately, sequential acquisition of fringe patterns is required; however, sequential fringe projection and acquisition perform poorly if the object is in motion during the projection. To overcome this limitation, we developed a novel method of FPP maned multispectral fringe projection profilometry (MFPP). The proposed method provides multispectral illumination patterns using a multispectral filter array (MFA) to generate multiple fringe patterns from a single illumination and capture the composite pattern using a single multispectral camera. Therefore, a single camera acquisition can provide multiple fringe patterns, and this directly increases the speed of imaging by a factor equal to the number of fringe patterns included in the composite pattern. Chapter 3 introduces this new technique and shows how it can be used to perform 3D profilometry at video frame rates. The first attempt at MFPP significantly improved acquisition speed by a factor of eight by providing eight different fringe patterns in four different directions, which permits the system to detect more morphological details. However, the phase retrieval algorithm used in this method was based on the spatial phase stepping process that had a few limitations, including high sensitive to the quality of the fringe patterns and being a global process, as it spreads the effect of the noisy pixels across the entire result. To overcome this limitation, Chapter 4 introduces an enhanced version of MFPP that utilized a specially designed multispectral illuminator to simultaneously project four p/2 phase-shifted fringe patterns onto an object. Combined with a spectrally matched multispectral camera, the refined MFPP method provided the needed data for the temporal phase retrieval algorithm using only a single camera exposure. Thus, it delivers high accuracy and pixel-wise measurement (thanks to the temporal phase stepping algorithms) while maintaining a high sampling rate for profilometry of moving objects. In conclusion, MFPP overcomes the limitations of sequential sampling imposed by FPP with temporal phase extraction without sacrificing data quality or accuracy of the reconstructed surface profiles. Since MFPP utilizes no moving parts and is based on MEMS technology, it is applicable to miniaturization for use in mobile devices and may be useful for space-constrained applications such as robotic surgery

Application of X-ray Grating Interferometry to Polymer/Flame Retardant Blends in Additive Manufacturing

X-ray grating interferometry is a nondestructive tool for visualizing the internal structures of samples. Image contrast can be generated from the absorption of X-rays, the change in phase of the beam and small-angle X-ray scattering (dark-field). The attenuation and differential phase data obtained complement each other to give the internal composition of a material and large-scale structural information. The dark-field signal reveals sub-pixel structural detail usually invisible to the attenuation and phase probe, with the potential to highlight size distribution detail in a fashion faster than conventional small-angle scattering techniques. This work applies X-ray grating interferometry to the study of additively manufactured polymeric objects. Additively manufactured bunnies made from single material—acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA)—were studied by grating-based X-ray interferometric two-dimensional imaging and tomography. The dark-field images detected poor adhesion in the plane perpendicular to the build plate. Curvature analysis of the sample perimeter revealed a slightly higher propensity to errors in regions of higher curvature. Incorporation of flame-retardant molecules to near-surface regions of otherwise flammable objects through the fused deposition modeling additive manufacturing technique was also explored. The anticipated advantage was efficient use of the flame retardants while keeping them away from the surface for safety. To determine heat propagation effects, two-dimensional grating-based interferometry imaging at LSU CAMD was used to study heated samples. The focus was on the dark-field signals to highlight voids and gaps arising from layer delamination or gasification of chemical components. The resulting differential phase and dark-field x images were tainted by fringes attributed to inaccuracies in the grating-step position. Attempts to correct this will be presented. Interferometric tomography was also carried out on the heated samples using the W. M. Keck interferometric system at LSU. Grating-based interferometry was also used to probe scattering structure sizes of heated samples. Comparison of the data with the conventional small-angle x-ray scattering technique, SAXS, is being pursued. The results obtained so far from the above-mentioned experimental works are presented in this document

Phase extraction of non-stationary signals produced in dynamic interferometry involving speckle waves

It is now widely acknowledged, among communities of researchers and engineers of very different horizons, that speckle interferometry (SI) offers powerful techniques to characterize mechanical rough surfaces with a submicronic accuracy in static or quasi-static regime, when small displacements are involved (typically several microns or tens of microns). The issue of dynamic regimes with possibly large deformations (typically several hundreds of microns) is still topical and prevents an even more widespread use of speckle techniques. This is essentially due to the lack of efficient processing schemes able to cope with non-stationary AM-FM interferometric signals. In addition, decorrelation-induced phase errors represent an hindrance to accurate measurement when such large displacements and classical fringe analysis techniques are considered. This work is an attempt to address those issues and to endeavor to make the most of speckle interferometry signals. Our answers to those problems are located on two different levels. First of all, we adopt the temporal analysis approach, i.e. the analysis of the temporal signal of each pixel of the sensor area used to record the interferograms. A return to basics of phase extraction is operated to properly identify the conditions under which the computed phase is meaningful and thus give some insight on the physical phenomenon under analysis. Due to their intrinsic non-stationary nature, a preprocessing tool is missing to put the SI temporal signals in a shape which ensures an accurate phase computation, whichever technique is chosen. This is where the Empirical Mode Decomposition (EMD) intervenes. This technique, somehow equivalent to an adaptive filtering technique, has been studied and tailored to fit with our expectations. The EMD has shown a great ability to remove efficiently the random fluctuating background intensity and to evaluate the modulation intensity. The Hilbert tranform (HT) is the natural quadrature operator. Its use to build an analytical signal from the so-detrended SI signal, for subsequent phase computation, has been studied and assessed. Other phase extraction techniques have been considered as well for comparison purposes. Finally, our answer to the decorrelation-induced phase error relies on the well-known result that the higher the pixel modulation intensity, the lower the random phase error. We took benefit from this result – not only linked to basic SNR considerations, but more specifically to the intrinsic phase structure of speckle fields – with a novel approach. The regions within the pixel signal history classified as unreliable because under-modulated, are purely and simply discarded. An interpolation step with the Delaunay triangulation is carried out with the so-obtained non-uniformly sampled phase maps to recover a smooth phase which relies on the most reliable available data. Our schemes have been tested and discussed with simulated and experimental SI signals. We eventually have developed a versatile, accurate and efficient phase extraction procedure, perfectly able to tackle the challenge of dynamic behaviors characterization, even for displacements and/or deformations beyond the classical limit of the correlation dimensions

The quantitative analysis of transonic flows by holographic interferometry

This thesis explores the feasibility of routine transonic flow analysis by holographic interferometry. Holography is potentially an important quantitative flow diagnostic, because whole-field data is acquired non-intrusively without the use of particle seeding. Holographic recording geometries are assessed and an image plane specular illumination configuration is shown to reduce speckle noise and maximise the depth-of-field of the reconstructed images. Initially, a NACA 0012 aerofoil is wind tunnel tested to investigate the analysis of two-dimensional flows. A method is developed for extracting whole-field density data from the reconstructed interferograms. Fringe analysis errors axe quantified using a combination of experimental and computer generated imagery. The results are compared quantitatively with a laminar boundary layer Navier-Stokes computational fluid dynamics (CFD) prediction. Agreement of the data is excellent, except in the separated wake where the experimental boundary layer has undergone turbulent transition. A second wind tunnel test, on a cone-cylinder model, demonstrates the feasibility of recording multi-directional interferometric projections using holographic optical elements (HOE’s). The prototype system is highly compact and combines the versatility of diffractive elements with the efficiency of refractive components. The processed interferograms are compared to an integrated Euler CFD prediction and it is shown that the experimental shock cone is elliptical due to flow confinement. Tomographic reconstruction algorithms are reviewed for analysing density projections of a three-dimensional flow. Algebraic reconstruction methods are studied in greater detail, because they produce accurate results when the data is ill-posed. The performance of these algorithms is assessed using CFD input data and it is shown that a reconstruction accuracy of approximately 1% may be obtained when sixteen projections are recorded over a viewing angle of ±58°. The effect of noise on the data is also quantified and methods are suggested for visualising and reconstructing obstructed flow regions

Automated calibration of multi-sensor optical shape measurement system

A multi-sensor optical shape measurement system (SMS) based on the fringe projection method and temporal phase unwrapping has recently been commercialised as a result of its easy implementation, computer control using a spatial light modulator, and fast full-field measurement. The main advantage of a multi-sensor SMS is the ability to make measurements for 360° coverage without the requirement for mounting the measured component on translation and/or rotation stages. However, for greater acceptance in industry, issues relating to a user-friendly calibration of the multi-sensor SMS in an industrial environment for presentation of the measured data in a single coordinate system need to be addressed. The calibration of multi-sensor SMSs typically requires a calibration artefact, which consequently leads to significant user input for the processing of calibration data, in order to obtain the respective sensor's optimal imaging geometry parameters. The imaging geometry parameters provide a mapping from the acquired shape data to real world Cartesian coordinates. However, the process of obtaining optimal sensor imaging geometry parameters (which involves a nonlinear numerical optimization process known as bundle adjustment), requires labelling regions within each point cloud as belonging to known features of the calibration artefact. This thesis describes an automated calibration procedure which ensures that calibration data is processed through automated feature detection of the calibration artefact, artefact pose estimation, automated control point selection, and finally bundle adjustment itself. [Continues.

Optical techniques applied to measurements in art

Optical diagnostic techniques are particularly attractive for the non-destructive detection of incipient damage and the evaluation of the state of surface decay. Non-contact, high precision measurements of the shape and deformation of an artifact can be performed using laser methods based on holographic and speckle interferometry. [Continues.

Measuring aberrations in lithographic projection systems with phase wheel targets

A significant factor in the degradation of nanolithographic image fidelity is optical wavefront aberration. Aerial image sensitivity to aberrations is currently much greater than in earlier lithographic technologies, a consequence of increased resolution requirements. Optical wavefront tolerances are dictated by the dimensional tolerances of features printed, which require lens designs with a high degree of aberration correction. In order to increase lithographic resolution, lens numerical aperture (NA) must continue to increase and imaging wavelength must decrease. Not only do aberration magnitudes scale inversely with wavelength, but high-order aberrations increase at a rate proportional to NA2 or greater, as do aberrations across the image field. Achieving lithographic-quality diffraction limited performance from an optical system, where the relatively low image contrast is further reduced by aberrations, requires the development of highly accurate in situ aberration measurement. In this work, phase wheel targets are used to generate an optical image, which can then be used to both describe and monitor aberrations in lithographic projection systems. The use of lithographic images is critical in this approach, since it ensures that optical system measurements are obtained during the system\u27s standard operation. A mathematical framework is developed that translates image errors into the Zernike polynomial representation, commonly used in the description of optical aberrations. The wavefront is decomposed into a set of orthogonal basis functions, and coefficients for the set are estimated from image-based measurements. A solution is deduced from multiple image measurements by using a combination of different image sets. Correlations between aberrations and phase wheel image characteristics are modeled based on physical simulation and statistical analysis. The approach uses a well-developed rigorous simulation tool to model significant aspects of lithography processes to assess how aberrations affect the final image. The aberration impact on resulting image shapes is then examined and approximations identified so the aberration computation can be made into a fast compact model form. Wavefront reconstruction examples are presented together with corresponding numerical results. The detailed analysis is given along with empirical measurements and a discussion of measurement capabilities. Finally, the impact of systematic errors in exposure tool parameters is measureable from empirical data and can be removed in the calibration stage of wavefront analysis

Remote sensing based assessment of land cover and soil moisture in the Kilombero floodplain in Tanzania

Wetlands provide important ecological, biological, and social-economic services that are critical for human existence. The increasing demand for food, arable land shortage and changing climate conditions in East Africa have created a paradigm shift from upland cultivation to wetland use due to their year-round soil water availability. However, there is need to control and manage the activities within the wetlands to ensure sustainable use while negating any negative effects caused by these activities. This is implemented through the decisions made by the land managers within the wetlands. Providing the users of the wetlands with scientific knowledge acts as a support tool for policy-making geared towards the sustainable use of the wetlands. The overall research contains two main components: First, the need for timely land cover maps at a reasonable scale, and secondly, the assessment of soil moisture as a major contributor to agricultural production. The objectives of the study were to generate land cover maps from multi-sensor optical datasets and to assess the performance of single-polarized Sentinel-1 Gray Level Co-occurrence Matrix (GLCM) texture and Principal Component Analysis (PCA) features by applying multiple classification algorithms in a floodplain in the Kilombero catchment. Furthermore, soil moisture spatial-temporal patterns over three hydrological zones was assessed, estimation of soil moisture from radar data and generation of soil moisture products from global products was investigated. The correlation of the merged products to Normalized Difference Vegetation Index (NDVI) measures was also investigated. RapidEye, Sentinel-2 and Landsat images were used in determining the areal extents of four major land cover classes namely vegetated, bare, water and built up. The acquisition period of the images ranges from August 2013 to June 2015 for the RapidEye images, December 2015 to August 2016 for the Sentinel-2 images and 2013 to 2016 Landsat-8 images were included in the land cover time series dynamic study. However, the major challenge arising was cloud coverage and hence Sentinel-1 images were tested in the application of Synthetic Aperture Radar (SAR) in wetland mapping. Variograms were used in spatial-temporal assessment of soil moisture data collected from three hydrological zones, riparian, middle and fringe. A roughness parameter was derived from a semi-empirical model. Soil moisture was retrieved from TerraSAR-X and RadarSAT-2 with the retrieved roughness parameter as an input in a linear regression equation. Triple collocation was applied in error assessment of the global soil moisture products prior to development of a merged product. Cross-correlation was applied in relating NDVI to soil moisture. Optical data (RapidEye, Landsat-8, and Sentinel-2) generated land cover maps used in assessing the land cover dynamics over time. The land cover ratios were related to depth to groundwater. As the depth to groundwater reduced in June the bare land coverage was 45-57% while that of vegetation was 34-47%. In December when the depth to groundwater was highest, bare land coverage was 62-69% while that of the vegetated area was 27-25%. This indicates that depth of groundwater and vegetation coverage responds to seasonality. During the dry season, 68-81% of the total vegetation class is within the riparian zone. In the classification of the SAR images, the overall accuracies for the single polarized VV images ranged from 54-76%, 60-81% and 61-80% for Random Forest (RF), Neural Network (NN) and Support Vector Machine (SVM) respectively. GLCM features had overall accuracies of 64-86%, 65-88% and 65-86% for RF, NN, and SVM respectively. PCA derived images had similar overall accuracies of 68-92% for NN, RF, and SVM respectively. The PCA images had the highest overall accuracy for the entire time series indicating that reduction in the number of texture features to layers containing the maximum variance improves the accuracy. The standard deviation of soil moisture was noted to increase with increasing soil moisture. Soil texture plays a key role in soil moisture retention. The riparian fields had a high water content explained by the high clay and organic matter content. A roughness parameter was derived and utilized in the retrieval of soil moisture from SAR resulting to R2 of 0.88- 0.92 between observed and simulated soil moisture values from co-polarized RadarSAT-2 HH and TerraSAR-X HH and VV. Merged soil moisture product from FEWSNET Land Data Assimilation System_NOAH (FLDAS_NOAH), ECMWF Re-Analysis Interim (ERA-Interim) and Soil Moisture and Ocean Salinity (SMOS) and FLDAS_Variable Infiltration Capacity (VIC), ERA-Interim and SMOS had similar patterns attributed to FLDAS_NOAH and FLDAS_VIC forced by the same precipitation product (RFE). Cross-correlation of Moderate-resolution Imaging Spectrometer (MODIS) NDVI and the merged soil moisture products revealed a 2-month lag of NDVI. Hence, the relationship is useful in determining the Start of Season from soil moisture products. In conclusion, the successful land cover mapping of the study area demonstrated the use of satellite imagery for wetland characterization. The vast coverage and frequent acquisitions of optical and microwave remotely sensed data additionally make the approaches transferable to other locations and allow for mapping at larger scales. Soil moisture assessment from point data revealed varied soil moisture patterns whereas global remotely sensed and modeled products rather provide complementary information about growing conditions, and hence a situational assessment tool of potential of physical availability dimension of food security. This study forms a baseline upon which additional monitoring and assessment of the Kilombero wetland ecosystem can be performed with the current results marked as a reference. Moreover, the study serves as a demonstration case of remote sensing based approaches for land cover and soil moisture mapping, whose results are useful to stakeholders to aid in the implementation of adapted production techniques for yield optimization while minimizing the unsustainable use of the natural resources.Feuchtgebiete erbringen wichtige ökologische, biologische und sozial-ökonomische Dienstleistungen, welche entscheidend für das menschliche Dasein sind. Der steigende Bedarf an Nahrung, der Mangel an landwirtschaftlichen Nutzflächen und die Veränderung der klimatischen Bedingungen in Ostafrika haben zu einem Paradigmenwechsel vom Anbau im Hochland hin zur Nutzung von Feuchtgebieten geführt. Allerdings sind Kontrolle und Management der Aktivitäten in Feuchtgebieten notwendig, um die nachhaltige Nutzung zu sichern und negative Effekte dieser Aktivitäten zu vermeiden. Die Implementierung erfolgt durch die Landverwalter in den Feuchtgebieten. Den Nutzern von Feuchtgebieten wissenschaftliche Erkenntnisse bereitzustellen dient als Hilfsmittel zur politischen Entscheidungsfindung für die nachhaltige Feuchtgebietsnutzung. Die Forschung im Rahmen der Dissertation beinhaltet zwei Hauptkomponenten: erstens den Bedarf an aktuellen Landbedeckungskarten auf einer angemessenen Skalenebene und zweitens die Erfassung der Bodenfeuchte als wichtiger Einflussfaktor auf die landwirtschaftliche Produktion. Das Ziel der Untersuchung war, Landbedeckungskarten auf Grundlage von multisensorischen optischen Daten zu erstellen und die Eignung der Textur der einfach polarisierten Sentinel-1 Grauwertmatrix (GLCM) sowie der einer Hauptkomponentenanalyse (PCA) bei Anwendung unterschiedlicher Klassifikationsalgorithmen zu beurteilen. Des Weiteren wurden raum-zeitliche Bodenfeuchtemuster über drei hydrologische Zonen hinweg modelliert, die Bodenfeuchte aus Radardaten abgeleitet sowie die Erstellung von Bodenfeuchteprodukten auf Basis von globalen Produkten untersucht. Die Korrelation der Bodenfeuchteprodukte mit dem Normalisierten Differenzierten Vegetationsindex (NDVI) wurde ebenfalls analysiert. RapidEye, Sentinel-2 und Landsat Bilder wurden genutzt um die räumliche Ausdehnung der vier Hauptklassen (Vegetation, freiliegender Boden, Wasser und Bebauung) der Landbedeckung zu ermitteln. Für die Zeitreihenanalyse der der Landbedeckungsdynamik wurden RapidEye-Daten von August 2013 bis Juni 2015, Sentinel-2-Bilder von Dezember 2015 bis August 2016 und Landsat-8-Bilder von 2013 bis 2016 verwendet. Die größte Herausforderung war jedoch die Wolkenbedeckung, weshalb die Anwendung von Synthetic Aperture Radar (SAR) für die Feuchtgebietskartierung getestet wurde. Die gemessene Bodenfeuchte wurde mittels Variogrammen für die drei hydrologischen Zonen (Uferzone, Mitte und Randgebiete) raum-zeitlich interpoliert. Ein Rauhigkeitsparameter wurde aus einem semi-empirischen Modell hergeleitet. Die Bodenfeuchte wurde aus TerraSAR-X und RadarSAT-2- Bildern unter Verwendung des Rauhigkeitsparameters als Eingangsgröße in einer linearen Regression abgeleitet. Vor der Zusammenführung der Produkte wurde das globale Bodenfeuchteprodukt mithilfe von dreifacher Kollokation auf Fehler überprüft. Die Kreuzkorrelation zwischen NDVI und Bodenfeuchte wurde berechnet. Optische Daten (RapidEye, Landsat-8 und Sentinel-2) wurden genutzt, um die zeitliche Dynamik der Landbedeckung zu bestimmen. Die Landbedeckungsverhältnisse wurde mit der Höhe des Grundwasserspiegels korreliert. Ein hoher Grundwasserstand im Juni resultierte in 45-57% unbedecktem Boden, während der Anteil der Vegetation 34-47% betrug. Im Dezember, als der Grundwasserspiegel seinen Tiefststand hatte, erhöhte sich der Anteil des freiliegenden Bodens auf 62-69% und der Anteil der Vegetation verringerte sich auf 27-25%. Das zeigt, dass Grundwasserspiegel und Vegetation saisonalen Schwankungen unterworfen sind. Während der Trockenzeit liegen 68-81% der gesamten als Vegetation klassifizierten Fläche innerhalb der Uferzone. In der Klassifikation der SAR-Bilder liegt die Gesamtgenauigkeit der einfach polarisierten VV-Bilder im Rahmen von 54-76%, 60-81% und 61-80%, entsprechend für Random Forest (RF), Neuronale Netze (NN) und Support Vector Machine (SVM). Die GLCM ergab eine Gesamtgenauigkeit von 64-86%, 65-88% und 65-86% für RF, NN und SVM. Die über eine PCA abgeleiteten Bilder erreichten eine ähnliche Genauigkeit von 68-92% für NN, RF und SVM. Die PCA-Bilder weisen die höchste Gesamtgenauigkeit der gesamten Zeitreihe auf, was darauf hinweist, dass eine Reduktion von Textureigenschaften auf Layer der maximalen Varianz enthalten, die Genauigkeit erhöht. Die Standardabweichung der Bodenfeuchte stieg mit zunehmender Bodenfeuchte. Die Bodentextur spielt dabei eine Schlüsselrolle für das Wasserhaltevermögen des Bodens. Die Uferzone wies einen hohen Wassergehalt auf, was durch den hohen Anteil von Ton und Humus zu erklären ist. Die beobachteten und simulierten Bodenfeuchtewerte von co-polarisierten RadarSAT-2 HH, TerraSAR-X HH und VV Daten korrelieren mit einem R2 von 0.88 - 0.92. Die zusammengesetzten globalen Bodenfeuchteprodukte von FLDAS_NOAH, ERA-Interim sowie SMOS und FLDAS_VIC, ERA-Interim und SMOS zeigen ähnliche Muster wie FLDAS_NOAH und FLDAS_VIC, was über die Verwendung desselben Niederschlagsproduktes (RFE) zu erklären ist. Die Kreuzkorrelation von MODIS NDVI und den zusammengeführten Bodenfeuchteprodukten ergab eine zeitliche Verzögerung des NDVI von zwei Monaten. Dieser Zusammenhang kann daher bei der Bestimmung des Saisonbeginns aus Bodenfeuchtigkeitsprodukten nützlich sein. Zusammengefasst hat die Studie gezeigt, wie Satellitenbilder zur Charakterisierung von Wetlands genutzt werden können. Die große Abdeckung und häufige Aufnahme der optischen und Mikrowellen-Fernerkundungsdaten ermöglichen darüber hinaus die Übertragung der Ansätze auf weitere Gebiete und Kartierung auf größeren Skalen. Die Punktmessungen zeigen kleinräumige Muster der Bodenfeuchte, während globale Fernerkundungsprodukte und Modelle Informationen über die Wachstumsbedingungen liefern und somit ein Bewertungsinstrument der Ernährungssicherheit darstellen können. Weiterhin bildet die Studie eine Basis, auf der ein weitergehendes Monitoring und eine Bewertung des Feuchtgebietsökosystems durchgeführt werden kann. Sie ist ein Beispiel für fernerkundungsbasierte Ansätze zur Landbedeckungs- und Bodenfeuchtekartierung; ihre Ergebnisse sind nützlich, um Akteuren bei der Implementierung von Produktionstechniken zu unterstützen, welche die Erträge maximieren und gleichzeitig die nicht nachhaltige Nutzung der natürlichen Ressourcen minimieren

Statistical model based 3D shape prediction of postoperative trunks for non-invasive scoliosis surgery planning

One of the major concerns of scoliosis patients undergoing surgical treatment is the aesthetic aspect of the surgery outcome. It would be useful to predict the postoperative appearance of the patient trunk in the course of a surgery planning process in order to take into account the expectations of the patient. In this paper, we propose to use least squares support vector regression for the prediction of the postoperative trunk 3D shape after spine surgery for adolescent idiopathic scoliosis. Five dimensionality reduction techniques used in conjunction with the support vector machine are compared. The methods are evaluated in terms of their accuracy, based on the leave-one-out cross-validation performed on a database of 141 cases. The results indicate that the 3D shape predictions using a dimensionality reduction obtained by simultaneous decomposition of the predictors and response variables have the best accuracy.CIHR / IRS