The images in photogrammetry and remote sensing applications are always degraded images. The degradation lies in two aspects: geometry degradation and radiometry degradation. Aimed at the images captured in various scales, the causes of uneven illumination and uneven color are analyzed. Then

and OGC photogrammetry and remote sensing are subclasses of geographic information. The new ISO-standards as well as the Sensor Web Enablement and other initiatives of the OGC will be portrayed. Other organizations in this field like GEOSS or the European expert group EuroSDR, European Spatial Data

Remote Sensing (hereinafter referred to as the Society)

Laser scanner data is being increasingly used to obtain topographical and object height information for mapping and GIS tasks. Valuable information can be derived of the terrain and objects of a region. Different methods have been published to segment laser scanner data in order to extract information. This paper aims to discuss the extraction of buildings to transfer 2D building data into 3D building data of a higher accuracy. For this study, only laser scanner data and scanned topographical maps are to be used. The study can be divided into two parts: segmentation and detection. Firstly, the region-based segmentation method is used to delimit objects. Then a scanned topographic map, filtered and converted into a vector format, is utilised to locate already recorded houses. Buildings not contained in the map could be found by combination of segment attributes. The study shows promising results – more than 95 % of the buildings were detected. KURZFASSUNG: Laserscannerdaten werden im zunehmenden Maße zur Informationsgewinnung von topographischen und Objekthöhen für Karten

LiDAR data by combining mean shift with normalized cuts: two examples from urban areas

contract consultancy At last year‟s ILMF meeting, three key issues facing the industry were noted: i) recruitment; ii) training; iii) development of standards and best practices. In 2004, the Applied Geomatics Research Group embarked on a market research project to evaluate LiDAR industry training needs. The findings from this study were implemented in the curriculum at the Centre of Geographic Sciences in 2006 and published in the May, 2007 edition of

We present an approach to 3D scene flow estimation, which exploits that in realistic scenarios image motion is frequently dominated by observer motion and independent, but rigid object motion. We cast the dense estimation of both scene structure and 3D motion from sequences of two or more views as a single energy minimization problem. We show that agnostic smoothness priors, such as the popu-lar total variation, are biased against motion discontinu-ities in viewing direction. Instead, we propose to regularize by encouraging local rigidity of the 3D scene. We derive a local rigidity constraint of the 3D scene flow and define a smoothness term that penalizes deviations from that con-straint, thus favoring solutions that consist largely of rigidly moving parts. Our experiments show that the new rigid mo-tion prior reduces the 3D flow error by 42 % compared to standard TV regularization with the same data term. 1.

Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction *

This paper approaches the problem of road extraction from three different directions. The first is the use of multiple scales. This combines detailed information of fine scale, like the markings, with abstract information of coarse scale, like the road network. The second direction is the extension of the multi-scale modeling with the context, i.e., the relations to other objects like buildings or trees. The context itself is split hierarchically into local context sketches, like occlusion shadow, which is modeling a tree casting a shadow on the road, and global context regions, i.e., open rural, suburb urban, and forest areas which comprise the whole image. The context information is very useful to focus the extraction. The third direction taken in this paper is the use of snakes. So-called ribbon snakes are used not only to extract roads in a robust manner in fine scale, but they can be also used to bridge gaps in the extracted roads due to occlusions or shadows cast by buildings and trees. Practical examples show the validity of the approach. 1

Abstract. A new method for the automated extraction of pipelines and other linear objects from Synthetic Aperture Radar (SAR) scenes is presented. It combines intensity data with coherence data from an interferometric evaluation of a SAR scene pair. The fusion is based on Bayesian statistics and is part of a Markov random � eld (MRF) model for line extraction. Both intensity and coherence data are evaluated using rotating templates. The diVerent statistical properties of intensity and coherence are taken into account by a multiplicative noise model and an additive noise model respectively. The MRF model introduces prior knowledge about the continuity and the narrowness of lines. Posterior odds resulting from the MRF method are input to a method based on ziplock snakes for linear object extraction. This processing step is controlled interactively which is necessary as fully automatic processing of the given noisy data does not provide suYciently predictable results. The method is applied to data of the ERS tandem mission. 1.