PERFORMANCE EVALUATION OF THERMOGRAPHIC CAMERAS FOR PHOTOGRAMMETRIC DOCUMENTATION OF HISTORICAL BUILDINGS

Thermographic cameras record temperatures emitted by objects in the infraredregion. These thermal images can be used for texture analysis and deformationcaused by moisture and isolation problems. For accurate geometric survey of thedeformations, the geometric calibration and performance evaluation of thethermographic camera should be conducted properly. In this study, an approach isproposed for the geometric calibration of the thermal cameras for the geometricsurvey of deformation caused by moisture. A 3D test object was designed and usedfor the geometric calibration and performance evaluation. The geometric calibrationparameters, including focal length, position of principal point, and radial andtangential distortions, were determined for both the thermographic and the digitalcamera. The digital image rectification performance of the thermographic camerawas tested for photogrammetric documentation of deformation caused by moisture.The obtained results from the thermographic camera were compared with the resultsfrom digital camera based on the experimental investigation performed on a studyarea

AUTOMATIC BUILDING EXTRACTION USING LiDAR AND AERIAL PHOTOGRAPHS

ABSTRACT This paper presents an automatic building extraction approach using LiDAR data and aerial photographs from a multi-sensor system positioned at the same platform. The automatic building extraction approach consists of segmentation, analysis and classification steps based on object-based image analysis. The chessboard, contrast split and multi-resolution segmentation methods were used in the segmentation step. The determined object primitives in segmentation, such as scale parameter, shape, completeness, brightness, and statistical parameters, were used to determine threshold values for classification in the analysis step. The rule-based classification was carried out with defined decision rules based on determined object primitives and fuzzy rules. In this  study, hierarchical classification was preferred. First, the vegetation and ground classes were generated; the building class was then extracted. The NDVI, slope and Hough images were generated and used to avoid confusing the building class with other classes. The intensity images generated from the LiDAR data and morphological operations were utilized to improve the accuracy of the building class. The proposed approach achieved an overall accuracy of approximately 93% for the target class in a suburban neighborhood, which was the study area. Moreover, completeness (96.73%) and correctness (95.02%) analyses were performed by comparing the automatically extracted buildings and reference data.

URBAN MODELLING PERFORMANCE OF NEXT GENERATION SAR MISSIONS

In synthetic aperture radar (SAR) technology, urban mapping and modelling have become possible with revolutionary missions TerraSAR-X (TSX) and Cosmo-SkyMed (CSK) since 2007. These satellites offer 1m spatial resolution in high-resolution spotlight imaging mode and capable for high quality digital surface model (DSM) acquisition for urban areas utilizing interferometric SAR (InSAR) technology. With the advantage of independent generation from seasonal weather conditions, TSX and CSK DSMs are much in demand by scientific users. The performance of SAR DSMs is influenced by the distortions such as layover, foreshortening, shadow and double-bounce depend up on imaging geometry. In this study, the potential of DSMs derived from convenient 1m high-resolution spotlight (HS) InSAR pairs of CSK and TSX is validated by model-to-model absolute and relative accuracy estimations in an urban area. For the verification, an airborne laser scanning (ALS) DSM of the study area was used as the reference model. Results demonstrated that TSX and CSK urban DSMs are compatible in open, built-up and forest land forms with the absolute accuracy of 8–10 m. The relative accuracies based on the coherence of neighbouring pixels are superior to absolute accuracies both for CSK and TSX

Orientation of Airborne Laser Scanning Point Clouds with Multi-View, Multi-Scale Image Blocks

Comprehensive 3D modeling of our environment requires integration of terrestrial and airborne data, which is collected, preferably, using laser scanning and photogrammetric methods. However, integration of these multi-source data requires accurate relative orientations. In this article, two methods for solving relative orientation problems are presented. The first method includes registration by minimizing the distances between of an airborne laser point cloud and a 3D model. The 3D model was derived from photogrammetric measurements and terrestrial laser scanning points. The first method was used as a reference and for validation. Having completed registration in the object space, the relative orientation between images and laser point cloud is known. The second method utilizes an interactive orientation method between a multi-scale image block and a laser point cloud. The multi-scale image block includes both aerial and terrestrial images. Experiments with the multi-scale image block revealed that the accuracy of a relative orientation increased when more images were included in the block. The orientations of the first and second methods were compared. The comparison showed that correct rotations were the most difficult to detect accurately by using the interactive method. Because the interactive method forces laser scanning data to fit with the images, inaccurate rotations cause corresponding shifts to image positions. However, in a test case, in which the orientation differences included only shifts, the interactive method could solve the relative orientation of an aerial image and airborne laser scanning data repeatedly within a couple of centimeters

From Boatyard to Museum: 3D laser scanning and digital modelling of the Qatar Museums watercraft collection, Doha, Qatar

This is the final version of the article. Available from Wiley via the DOI in this record.This article presents the results of a project to 3D laser scan and digitally model 14 watercraft from the Qatar Museums collection, comprising a range of regional vessels: most had not been surveyed previously. The project used the resulting point clouds generated 2D naval lines and orthographic records of the vessels in their current condition, and photorealistic 3D digital models for gallery display. This case study provides illustrative examples of the intermediate stages and final outputs. It assesses the pros and cons of 3D laser scanning as a survey technology for nautical scholars in terms of the time, cost, and skillset, as well as logistical considerations. It also compares the accuracy of traditional hand survey methods.We wish to thank QM for enabling and funding this research (Grant number SL-05894)

Efficient three-dimensional reconstruction of aquatic vegetation geometry: Estimating morphological parameters influencing hydrodynamic drag

Aquatic vegetation can shelter coastlines from energetic waves and tidal currents, sometimes enabling accretion of fine sediments. Simulation of flow and sediment transport within submerged canopies requires quantification of vegetation geometry. However, field surveys used to determine vegetation geometry can be limited by the time required to obtain conventional caliper and ruler measurements. Building on recent progress in photogrammetry and computer vision, we present a method for reconstructing three-dimensional canopy geometry. The method was used to survey a dense canopy of aerial mangrove roots, called pneumatophores, in Vietnam’s Mekong River Delta. Photogrammetric estimation of geometry required 1) taking numerous photographs at low tide from multiple viewpoints around 1 m2 quadrats, 2) computing relative camera locations and orientations by triangulation of key features present in multiple images and reconstructing a dense 3D point cloud, and 3) extracting pneumatophore locations and diameters from the point cloud data. Step 3) was accomplished by a new ‘sector-slice’ algorithm, yielding geometric parameters every 5 mm along a vertical profile. Photogrammetric analysis was compared with manual caliper measurements. In all 5 quadrats considered, agreement was found between manual and photogrammetric estimates of stem number, and of number × mean diameter, which is a key parameter appearing in hydrodynamic models. In two quadrats, pneumatophores were encrusted with numerous barnacles, generating a complex geometry not resolved by hand measurements. In remaining cases, moderate agreement between manual and photogrammetric estimates of stem diameter and solid volume fraction was found. By substantially reducing measurement time in the field while capturing in greater detail the 3D structure, photogrammetry has potential to improve input to hydrodynamic models, particularly for simulations of flow through large-scale, heterogenous canopies

BUILDING EXTRACTION USING MULTI SENSOR SYSTEMS

In this study, the automatic building extraction is aimed using object-based image analysis method with multi sensor system includes LiDAR, digital camera and GPS/IMU. The image processing techniques, segmentation and classification methods were used for automatic object extraction with defined rule set. The proposed method based on object based classification to overcome the limitation of traditional pixel based classification such as confusion of classes. The generated Digital Surface Model (DSM) from LiDAR point cloud was used to separate building and vegetation classes. The morphologic filters were utilized also optimization of mixed classes. In our proposed approach for building extraction, multi-resolution, contrast-difference and chessboard segmentations were applied. The object-based classification method was preferred in classification process with defined fuzzy rules. First, vegetation and ground classes were generated than building regions were derived with using the results of the classification and segmentation. The data set was obtained from the project of "NABUCCO Gas Pipeline Project". The data set actually was collected for corridor mapping of pipeline which will link the Eastern border of Turkey, to Baumgarten in Austria via Bulgaria, Romania and Hungary. The study area is a suburban neighborhood located in the city of Sivas, Turkey. The Leica ALS60 LiDAR system, DiMAC, Dalsa Area Bayer RGB Charge Coupled (CCD) Camera and GPS and CUS6 IMU system were used for data collection. The additional data sets were generated with point cloud collected by LiDAR and RGB images from digital camera. The rule sets for automatic building extraction were developed in Definiens e-Cognition Developer 8.64 program system. To evaluate the performance of proposed automatic building extraction approach, reference data set was generated with digitizing of extracted building over the orthoimage. The accuracy assessment was performed with completeness and correctness analyses. Based on the completeness and accuracy analysis, the success rates of 83.08% for completeness and 85.51% for correctness were achieved