Integration of LIDAR and IFSAR for mapping

LiDAR and IfSAR data is now widely used for a number of applications, particularly those needing a digital elevation model. The data is often complementary to other data such as aerial imagery and high resolution satellite data. This paper will review the current data sources and the products and then look at the ways in which the data can be integrated for particular applications. The main platforms for LiDAR are either helicopter or fixed wing aircraft, often operating at low altitudes, a digital camera is frequently included on the platform, there is an interest in using other sensors such as 3 line cameras of hyperspectral scanners. IfSAR is used from satellite platforms, or from aircraft, the latter are more compatible with LiDAR for integration. The paper will examine the advantages and disadvantages of LiDAR and IfSAR for DEM generation and discuss the issues which still need to be dealt with. Examples of applications will be given and particularly those involving the integration of different types of data. Examples will be given from various sources and future trends examined

A weighted least squares solution for space intersection of spaceborne stereo SAR data

The use of stereoscopic SAR images offers an alternative to interferometric SAR for the generation of digital elevation models (DEMs). The stereo radargrammetric method is robust and can generate DEMs of sufficient accuracy to geocode SAR images. Previous work has shown that ground coordinates with accuracy of four times the resolution cell can be obtained from ERS data without using any ground control points (GCPs), where the high accuracy of the orbit and satellite position of the order of metres introduce insignificant errors into the intersection procedure. The orbit data for RADARSAT is not as accurate as that for ERS, and the perpendicular relationship between the resultant velocity vector and the resultant range vector is uncertain in terms of image geometry. Hence, it is necessary to refine the method to allow for possible errors. This paper introduces a weighted space intersection algorithm based on an analysis of the predicted errors. A radargrammetric error model for observation errors is also formulated to predict the accuracy of the algorithm. The revised method can be used without any GCPs, but this can lead to systematic errors due to less accurate orbit data, and it has been found that the use of two GCPs provides a reasonable solution. The method is insensitive to the spatial distribution of GCPs, which is often critical in traditional methods. The error statistics of the results generated from 32 independent check points, distributed through the entire SAR image, approach the predicted errors and give positional accuracy of 38 m in three dimensions

A Graph-based Technique for Higher Order Topological Data Structure Visualisation

Esta publicação foi agraciada com o prémio GISRUK 2005 “Whittles Publishing” Best Paper Award.Interpretation and analysis of spatial phenomena is a highly time consuming and laborious task in several fietds of the Geomatics world (Anders et al., 1999). That is why the automation of those tasks is especially needed in areas such as Geographical Information Science (GlScience). Carrying out these tasks in the context of an urban scene is particulariy challenging given its complexity: relatively small component elements and itt"it g"nrially complei spatial pattern (Eyton, 1993, and Barr & Barnsley, 1996, both cited in Barnsley and Barr, 1997). Topology is a particularly important research area in the field of GlScience, for it is a central àefining feature of a geographical information system (GIS). But, as far as topological relàtionships between spatial objects are concerned, "generally speaking .ottt.Àporary desktop bIS packages do not support further information beyond the first level oi adjâcency" (Theobald, 2001). Therefore, this research project focused on scene analysis bi buiiding up a technique for the better understanding of topological relationships between vector-based GIS objects, beyond the fnst level of adjacency. Another initial interest was to investigate the possible use of graph theory for this purpose. To date, this mathematical framework has been used in different applications in a wide range of fields to represent connections and relationships between spatial entities. Several u,rtùo6 (including Laurini and Thompson, 1992) have maintained that "this particular tool is extremely valuable and efficient in storing and describing the spatial structure of geographicil entities and their spatial arrangement". Theobald (2001) added that "concepts àf gruptt theory allow us to extend the standard notion of adjacency". The aim of retrieving structured information translated into more meaningful homogeneous regions, for instancJ fro* an initial unstructured data set, may be achieved by identifuing mJaningful structures within the initial random collection of objects and by understanding the spatial arrangement between them. We believe that applying graph theory and carrying out graph analysis may accomplish this

A graph-based algorithm to define urban topology from unstructured geospatial data

Interpretation and analysis of urban topology are particularly challenging tasks given the complex spatial pattern of the urban elements, and hence their automation is especially needed. In terms of the urban scene meaning, the starting point in this study is unstructured geospatial data, i.e. no prior knowledge of the geospatial entities is assumed. Translating these data into more meaningful homogeneous regions can be achieved by detecting geographic features within the initial random collection of geospatial objects, and then by grouping them according to their spatial arrangement. The techniques applied to achieve this are those of graph theory applied to urban topology analysis within GIS environment. This article focuses primarily on the implementation and algorithmic design of a methodology to define and make urban topology explicit. Conceptually, such procedure analyses and interprets geospatial object arrangements in terms of the extension of the standard notion of the topological relation of adjacency to that of containment: the so-called ‘containment-first search’. LiDAR data were used as an example scenario for development and test purposes