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

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

AUTOMATIC 3D BUILDING MODEL GENERATIONS WITH AIRBORNE LiDAR DATA

LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified that automatic 3D building models can be generated successfully using raw LiDAR point cloud data

ALS model-based comparison of Cosmo-SkyMed and TerraSAR-X HS DSMs on varied land forms

Since 2007, the revolutionary X-band synthetic aperture radar (SAR) satellites Cosmo-SkyMed (CSK) and TerraSAR-X (TSX) have been collecting high-resolution images that are convenient for digital surface model (DSM) acquisition by the interferometric SAR (InSAR) technique. In this study, the potential of DSMs derived from CSK and TSX high-resolution spotlight (HS) images is thoroughly analysed regarding the basic accuracy metrics absolute vertical accuracy (AVA) and relative vertical accuracy (RVA). Utilising convenient InSAR pairs, 2 m gridded DSMs are generated in Istanbul and validated with model-based comparisons using an actual airborne laser scanning (ALS) DSM. Results show that CSK and TSX DSMs are compatible in open, built-up and forest land forms. The AVAs are between 8 m and 10 m based on standard deviation of height discrepancies against the ALS model. The RVAs, calculated by the coherence of neighbouring pixels for each DSM, are superior to AVAs for both CSK and TSX. © 2016 Mapping Sciences Institute, Australia and Surveying and Spatial Sciences Institute

CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS

LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features) and have been tested in the study area in Zekeriyaköy, Istanbul which includes the partly open areas, forest areas and many types of the buildings. The data set used in this research obtained from Istanbul Metropolitan Municipality which was collected with ‘Riegl LSM-Q680i’ full-waveform laser scanner with the density of 16 points/m2. The proposed automatic point based Approach 1 and Approach 2 classifications successfully produced the ground, building and vegetation classes which were very similar although different features were used

Assessment of interferometric dems from terrasar-X stripmap and spotlight stereopairs: Case study in Istanbul

TerraSAR-X (TSX) has led to a new era for synthetic aperture radar (SAR) imaging, offering high-resolution (1 m) data that was not previously available from space. The TSX X-band sensor can be operated in three interferometrically compatible imaging modes: Stripmap (SM), high-resolution Spotlight (HS) and ScanSAR. Due to the low resolution of ScanSAR (16 to 30 m), SM and HS images are preferred for resolution-based applications. This paper attempts to validate and compare the digital elevation models (DEMs) derived from TSX SM and HS image pairs. Digital surface models (DSMs) were generated by interferometry in a part of Istanbul with a variety of ground types. These DSMs were converted to DEMs by filtering and then compared with a reference DEM produced from aerial photographs. The quality assessment was performed using advanced analyses concerned with accuracy and morphologic detail. The results of both absolute and relative accuracy analyses clearly reveal that the quality of the TSX HS DEM is better than SM and very consistent with the reference model. © 2014 The Remote Sensing and Photogrammetry Society and John Wiley & Sons Ltd

PERFORMANCE EVALUATION OF THERMOGRAPHIC CAMERAS FOR PHOTOGRAMMETRIC MEASUREMENTS

The aim of this research is the performance evaluation of the termographic cameras for possible use for photogrammetric documentation and deformation analyses caused by moisture and isolation problem of the historical and cultural heritage. To perform geometric calibration of the termographic camera, the 3D test object was designed with 77 control points which were distributed in different depths. For performance evaluation, Flir A320 termographic camera with 320 × 240 pixels and lens with 18 mm focal length was used. The Nikon D3X SLR digital camera with 6048 × 4032 pixels and lens with 20 mm focal length was used as reference for comparison. The size of pixel was 25 μm for the Flir A320 termographic camera and 6 μm for the Nikon D3X SLR digital camera. The digital images of the 3D test object were recorded with the Flir A320 termographic camera and Nikon D3X SLR digital camera and the image coordinate of the control points in the images were measured. The geometric calibration parameters, including the focal length, position of principal points, radial and tangential distortions were determined with introduced additional parameters in bundle block adjustments. The measurement of image coordinates and bundle block adjustments with additional parameters were performed using the PHIDIAS digital photogrammetric system. The bundle block adjustment was repeated with determined calibration parameter for both Flir A320 termographic camera and Nikon D3X SLR digital camera. The obtained standard deviation of measured image coordinates was 9.6 μm and 10.5 μm for Flir A320 termographic camera and 8.3 μm and 7.7 μm for Nikon D3X SLR digital camera. The obtained standard deviation of measured image points in Flir A320 termographic camera images almost same accuracy level with digital camera in comparison with 4 times bigger pixel size. The obtained results from this research, the interior geometry of the termographic cameras and lens distortion was modelled efficiently with proposed approach for geometric calibration