Height gradient approach for occlusion detection in UAV imagery

The use of Unmanned Aerial Vehicle (UAV) significantly increased in the last years. It is used for several different applications, such as mapping, publicity, security, natural disasters assistance, environmental monitoring, 3D building model generation, cadastral survey, etc. The imagery obtained by this kind of system has a great potential. To use these images in true orthophoto generation projects related to urban scenes or areas where buildings are present, it is important to consider the occlusion caused by surface height variation, platform attitude, and perspective projection. Occlusions in UAV imagery are usually larger than in conventional airborne dataset due to the low-altitude and excessive change in orientation due to the low-weight and wind effects during the flight mission. Therefore, this paper presents a method for occlusion detection together with some obtained results for images acquired by a UAV platform. The proposed method shows potential in occlusion detection and true orthophoto generation401W4263268International Conference on Unmanned Aerial Vehicles in Geomatic

3D classification of crossroads from multiple aerial images using markov random fields

The precise classification and reconstruction of crossroads from multiple aerial images is a challenging problem in remote sensing. We apply the Markov Random Fields (MRF) approach to this problem, a probabilistic model that can be used to consider context in classification. A simple appearance-based model is combined with a probabilistic model of the co-occurrence of class label at neighbouring image sites to distinguish up to 14 different classes that are relevant for scenes containing crossroads. The parameters of these models are learnt from training data. We use multiple overlap aerial images to derive a digital surface model (DSM) and a true orthophoto without moving cars. From the DSM and the orthophoto we derive feature vectors that are used in the classification. One of the features is a car confidence value that is supposed to support the classification when the road surface is occluded by static cars. Our approach is evaluated on a dataset of airborne photos of an urban area by a comparison of the results to reference data. Whereas the method has problems in distinguishing classes having a similar appearance, it is shown to produce promising results if a reduced set of classes is considered, yielding an overall classification accuracy of 74.8%

Use of multi-angle high-resolution imagery and 3D information for urban land-cover classification: a case study on Istanbul

The BELSPO-MAMUD project focuses on the use of Remote Sensing data for measuring and modelling urban dynamics. Remote sensing is a wonderful tool to produce long time-series of high resolution maps of sealed surface useful for this purpose. In the urban context of Istanbul, a very dynamic city, recent high resolution satellite images and medium resolution images from the past have been exploited to calibrate and validate a regression-based sub-pixel classification method allowing this production. In this context it’s a tricky task for several reasons: prominent occurrence of shadowed and occluded areas and urban canyons, spectral confusions between urban and non-urban materials at ground and roof levels, moderately hilly relief ... To cope with these difficulties the combined use of three types of data may be helpful: diachronic (i), multi-angle and 3D data. A master multispectral and panchromatic QuickBird image and a panchromatic Ikonos stereopair, all acquired in March 2002, were used in combination with a multispectral and panchromatic Ikonos image of May 2005. A DSM was generated from the Ikonos stereopair and building vector file. It was used for orthorectification, building height estimation and classification procedure. The area covered by the high resolution products was divided in 3 partitions and each one was classified independently. This application demonstrates that recent high resolution land-cover classification produced using multi-date, multi-angle and DSM can be used to produce sealed surface maps from longer timeseries of medium resolution images over large urban areas enabling so the analysis of urban dynamics

Geometric Accuracy Assessments of Orthophoto Production from UAV Aerial Images

Orthophoto mosaic is assembled from aerial perspective images through a process called orthorectification, which eliminate photographic tilts and terrain relief effects. These orthorectified images have been resampled from the original ones that may have been prepared from a DTM which does not accurately model the surface. Meanwhile, some proprietary software such as Agisoft utilizes spatially dense 3D point clouds that are generated from a so called Structure from Motion technique to generate the orthophoto. The software provides a black-box method to regard these clouds as DSM, and it utilizes this surface model to project pixels from the original images. This paper investigates geometric accuracy of the produced orthophoto mosaic according to the American Society of Photogrammetry and Remote Sensing (ASPRS) standards. To minimize scale differences among images, a 35mm fixed-lens camera is mounted on a fixed-wing UAV platform. Flight missions are carried out at around 250m flying height controlled by a navigational grade sensor on board to provide spatial resolution of about 27mm. A number of orthophoto mosaics are produced by varying numbers of GCPs, flight paths configuration and terrain relief differences. The geometric accuracies are assessed through a provision of ICPs on each test field area. Coordinates deviations between the ICP and the corresponding orthophotos are framed into a RMSE figures. Within a 95% confidence level, it is revealed that a suitable orthophoto map scale is up to around 1:500. It is recommended that a cross flight configuration to achieve better results

Object-Based Integration of Photogrammetric and LiDAR Data for Automated Generation of Complex Polyhedral Building Models

This research is concerned with a methodology for automated generation of polyhedral building models for complex structures, whose rooftops are bounded by straight lines. The process starts by utilizing LiDAR data for building hypothesis generation and derivation of individual planar patches constituting building rooftops. Initial boundaries of these patches are then refined through the integration of LiDAR and photogrammetric data and hierarchical processing of the planar patches. Building models for complex structures are finally produced using the refined boundaries. The performance of the developed methodology is evaluated through qualitative and quantitative analysis of the generated building models from real data

Building Footprint Extraction from LiDAR Data and Imagery Information

This study presents an automatic method for regularisation of building outlines. Initially, building segments are extracted using a new fusion method. Data- and model-driven approaches are then combined to generate approximate building polygons. The core part of the method includes a novel data-driven algorithm based on likelihood equation derived from the geometrical properties of a building. Finally, the Gauss-Helmert and Gauss-Markov models adjustment are implemented and modified for regularisation of building outlines considering orthogonality constraints