ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada / In this paper, we propose a new, low complexity approach for the design of laser radar (LIDAR) systems for use in applications in which the system is wirelessly transmitting its data from a remote location back to a command center for reconstruction and viewing. Specifically, the proposed system collects random samples in different portions of the scene, and the density of sampling is controlled by the local scene complexity. The range samples are transmitted as they are acquired through a wireless communications link to a command center and a constrained absolute-error optimization procedure of the type commonly used for compressive sensing/sampling is applied. The key difficulty in the proposed approach is estimating the local scene complexity without densely sampling the scene and thus increasing the complexity of the LIDAR front end. We show here using simulated data that the complexity of the scene can be accurately estimated from the return pulse shape using a finite moments approach. Furthermore, we find that such complexity estimates correspond strongly to the surface reconstruction error that is achieved using the constrained optimization algorithm with a given number of samples.

A new approach to improve the determination of building boundaries through automatic processing of light detection and ranging (LIDAR) data is presented. The LIDAR data is processed and interpolated into a grayscale image of intensity values corresponding to height measurements. Ground measurements are separated from non-ground measurements by using a progressive morphological filter. With these measurements now distinct, further separation of non-ground measurements into building and non-building measurements is performed by growing regions with similar characteristics. These building areas are then refined, resulting in a ground plan representation of building boundaries, known as building footprints. Several algorithms are then implemented to clean these footprints. A new method is developed to analyze actual known satellite imagery in order to confirm identified building footprints.

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Volunteered geographic information (VGI) has received increased attention as a new paradigm for geographic information production, while light detection and ranging (LiDAR) data is widely applied to many fields. This study quantitatively compares LiDAR data and user-generated 3D building models created using Google Building Maker, and investigate the potential applications of the quantitative measures in support of rapid disaster damage assessment. User-generated 3D building models from Google Building Maker are compared with LiDAR-derived building models using 3D shape signatures. Eighteen 3D building models are created in Fremont, California using the Google Building Maker, and six shape functions (distance, angle, area, volume, slope, and aspect) are applied to the 18 LiDAR-derived building models and user-generated ones. A special case regarding the comparison between LiDAR data and building models with indented walls is also discussed. Based on the results, several conclusions are drawn, and limitations that require further study are also discussed.

The eastern side of the Guadalupe Mountains, located in New Mexico and west Texas, represents an erosional profile along the Capitan reef margin. A complete shelf-to-basin exposure of the Upper Permian Capitan shelf margin is found on the north wall of North McKittrick Canyon, which is nearly perpendicular to the Capitan reef margin. An excellent 2-D sequence stratigraphic framework for upper Permian backreef facies has been developed by previous workers for North McKittrick Canyon (Tinker, 1998) and Slaughter Canyon (Osleger, 1998), forming the basis for observations in this study. The goal of this study is to describe the sequence stratigraphic architecture of the Yates Formation, focusing on the Y4-Y6 high-frequency sequences (HFSs) found in the middle to outer shelf depositional systems, and to illustrate the use of airborne lidar data to quantitatively map at the cycle-scale. Seven measured sections were taken in North McKittrick Canyon. From airborne lidar, 3-D geometries of key sedimentary and structural features were mapped in Polyworks, in addition to the sequence boundaries delineating the Yates 4-6 HFSs. In general, major cycles exhibit asymmetry and shoal upward. Cycle boundaries are sometimes hard to delineate due to amalgamation, particularly in the shelf crest. High-frequency sequences are commonly asymmetric; they deepen and thicken upward toward the maximum flooding surface, and the boundaries between HFSs are usually marked by thick siltstones. Major HFS boundaries can be mapped across the entire dataset, and some component cycles can be observed for minimum distances of one kilometer in an updip-downdip direction. Also, some facies tract dimensions can be estimated directly from the lidar data. Measured sections indicate that the shelf crest facies tract shifts seaward with each successive HFS, while the outer shelf facies tract steps landward. Future work that could be done with the Y4-Y6 HFSs includes 8-10 more measured sections, collection of samples for thin sections, and tracing out of contacts between facies tracts. Extensive lidar data interpretation needs to be done so that digital outcrop models demonstrating facies distributions can be produced. This would enable the development of an outcrop analog model to mixed carbonate-siliciclastic reservoirs, which would be unprecedented in this area. / text

The recent growth in inexpensive laser scanning sensors has created entire fields of research aimed at processing this data. One application is determining the polygonal boundaries of roofs, as seen from an overhead view. The resulting building outlines have many commercial as well as military applications. My work in this area has created a segmentation algorithm where the descriptive features are computationally and theoretically simpler than previous methods. A support vector machine is used to segment data points using these features, and their use is not common for roof detection to date. Despite the simplicity of the feature calculations, the accuracy of our algorithm is similar to previous work. I also describe a basic polygonal extraction method, which is acceptable for basic roofs.

Railway Catenary systems play a crucial role in the safe and reliable transportation of goods and people throughout the world. Monitoring the catenary infrastructure is crucial for safety purposes and therefore requires inspections. However, the current inspection methods are not sufficient for detecting all possible failure modes. The use of lidar has been proposed to augment the current inspection methods. This research proposes two methods for the classification of various overhead catenary components, resulting from lidar data, both solely relying on the coordinates of the captured datapoints. The methods resulted from a literature analysis and the parameters were obtained trough experimentation with a small dataset. The methods were validated using a larger dataset of 22.5 km between Boden and Gällivare and achieved promising outcomes. The first method resulted in an F1 score of 93,37% was obtained with 87,39% accuracy, whereas the second method, using a simple morphological region filtered obtain an F1 score of 95,48% and an accuracy of 91,27%. The novel contributions of the processing of lidar data in railway infrastructure is the use of a simple morphological region filter and the use of surface variation, a geometric feature for the extraction of masts and bridges. Further research is advised into the computational efficiency and further classification of components in the overhead catenary system.

Les marais salés de la Baie du Mont-Saint-Michel forment un écosystème complexe et fragile. Afin de mieux comprendre l'organisation spatiale de la végétation de ces marais et de développer une vision synthétique de la dynamique de ce milieu, des données de télédétection optiques (orthophotographie) et altimétrique (LiDAR), ainsi que des observations de terrain ont été réunies et traitées conjointement. Le contrôle qualité des données LiDAR ayant mis en évidence un biais systématique dépendant de la hauteur et de la densité de la végétation, une correction, consistant à assigner à chaque pixel la valeur LIDAR minimale au sein d'un carré de 3x3 m, a été appliquée. Une importante base de données spatialisées a ensuite été constituée, comprenant la description en termes de végétation, d'altitude, de géomorphologie, de sédimentologie et d'utilisation agricole de près de 10 000 points d'observation répartis sur l'ensemble de la Baie. Des analyses fréquentielles ont permis de quantifier les liaisons entre les différentes espèces végétales, afin de définir les associations végétales caractéristiques des marais salés. Cette méthode a également permis de dresser la liste exhaustive des liaisons statistiques entre toutes les espèces végétales de la Baie du Mont-Saint-Michel et quatre facteurs écologiques : l'altitude, la géomorphologie, le contexte sédimentaire et le pâturage. Les variations de l'altitude moyenne des espèces végétales à l'échelle de la Baie s'expliquent par l'existence d'une double pente hydraulique qui engendre des altitudes plus élevées à l'Est et à l'Ouest de la Baie. Le contexte géomorphologique et le pâturage peuvent également modifier les valeurs d'altitude moyenne des espèces végétales, et profondément perturber le schéma classique des marais salés. La connaissance des liaisons entre les espèces végétales et les valeurs des facteurs écologiques peut constituer une aide importante à la cartographie des marais salés / In the Mont-Saint-Michel bay, the tidal salt marshes, also called schorre, are made up of low and dense vegetation, that is adapted to tidal flooding and high salinity, and that spreads quickly on the upper tidal flat. The study of this phenomenon requires a map of the vegetation and a simple way to update it. The aim of this study is to evaluate the potential of LiDAR data to enhance the comprehension of this ecosystem. The LiDAR data was first corrected from the systematic error due to the low vegetation that stops the laser beams before they reach the ground. A spatial data base was then created from 9811 observation points, including information on vegetation, altimetry, geomorphology, sedimentology and land use. Frequency analyses were performed on three locations characterized by different geomorphology, sedimentology and land use contexts, and in a second time at the scale of the entire Bay. The vegetation associations of the salt marsh were described, and the relations between each plant species and four ecological factors (altimetry, geomorphology, sedimentology and land use) were investigated. This study has shown the need to include in the mapping process of salt marsh vegetation LiDAR data as well as information on geomorphology, sedimentology and land use

Multi-spectral Satellite imagery, among remotely sensed data from airborne and spaceborne platforms, contained the NIR band information is the major source for the land- cover classification. The main purpose of aerial imagery is for thematic land-use/land-cover mapping which is rarely used for land cover classification. Recently, the newly developed digital aerial cameras containing NIR band with up to 10cm ultra high resolution makes the land-cover classification using aerial imagery possible. However, because the urban ground objects are so complex, multi-spectral imagery is still not sufficient for urban classification. Problems include the difficulty in discriminating between trees and grass, the misclassification of buildings due to diverse roof compositions and shadow effects, and the misclassification of cars on roads. Recently, aerial LiDAR (ULiUght UDUetection UAUnd URUanging) data have been integrated with remotely sensed data to obtain better classification results. The LiDAR-derived normalized digital surface models (nDSMs) calculated by subtracting digital elevation models (DEMs) from digital surface models (DSMs) becomes an important factor for urban classification. This study proposed an adaptive raw-data-based, surface-based LiDAR data-filtering algorithm to generate DEMs as the foundation of generating the nDSMs. According to the experiment results, the proposed adaptive LiDAR data-filtering algorithm not only successfully filters out ground objects in urban, forest, and mixed land cover areas but also derives DEMs within the LiDAR data measuring accuracy based on the absolute and relative accuracy evaluation experiments results. For the aerial imagery urban classification, this study first conducted maximum likelihood classification (MLC) experiments to identify features suitable for urban classification using LiDAR data and aerial imagery. The addition of LiDAR height data improved the overall accuracy by up to 28 and 18%, respectively, compared to cases with only red¡Vgreen¡Vblue (RGB) and multi-spectral imagery. It concludes that the urban classification is highly dependent on LiDAR height rather than on NIR imagery. To further improve classification, this study proposes a knowledge-based classification system (KBCS) that includes a three-level height, ¡§asphalt road, vegetation, and non-vegetation¡¨ (A¡VV¡VN) classification model, rule-based scheme and knowledge-based correction (KBC). The proposed KBCS improved overall accuracy by 12 and 7% compared to maximum likelihood and object-based classification, respectively. The classification results have superior visual interpretability compared to the MLC classified image. Moreover, the visual details in the KBCS are superior to those of the OBC without involving a selection procedure for optimal segmentation parameters.