Case Study: The Department of Surveying and Geo Information of the State Government of Lower Austria (GEOinfo) is currently developing a land-use and land-cover model for more than 20,000 km² of territory, encompassing bodies of water, forests, urban and rural areas. Utilizing a beta version of Definiens eCognition 8, the State Government developed a software application to detect and quantify changes in forests, buildings, field and water areas from airborne Light Detection and Ranging (LiDAR) data and orthophotos.

Infoterra Ltd uses Definiens Software to Create High Definition Land Cover Products
   
LandBase™ provides classification down to individual property level

Leicester, UK / Munich, Germany – March 5, 2008 – Infoterra Ltd, a leader in the provision of geospatial products and services, uses Definiens’ market leading object-based image analysis to produce land cover classifications at the highest resolution, down to individual property level.

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.

Landscapes are Complex Systems, which by their very nature necessitate a multiscale approach in their monitoring, modeling and management. To assess such broad extents, remote sensing technology is the primary provider of landscape sized data sets, and while tremendous progress has been made over the last thirty years in terms of improved resolution, data availability, and public awareness, the vast majority of remote sensing analytical applications still rely on basic image processing concepts: in particular, per-pixel classification in multi-dimensional feature space.

Polarimetric SAR data possess a high potential for classification of earth surface. Various publications demonstrate detailed analysis of soil and vegetation properties and characteristics of man made structures on selected examples. To ensure wider application of these developments, integration in commercial systems should be studied. Here, in a first approach, the object based image analysis eCognition is employed on alpha, entropy and anisotropy and the span of fully polarimetric L-Band SAR data of the German airborne sensor, E-SAR.

While remote sensing has made enormous progress over recent years and a variety of sensors now deliver medium and high resolution data on an operational basis, a vast majority of applications still rely on basic image processing concepts developed in the early 70s: classification of single pixels in a multi-dimensional feature space. Although the techniques are well developed and sophisticated variations include soft classifiers, sub-pixel classifiers and spectral un-mixing techniques, it is argued that they do not make use of spatial concepts.

Analysing landscape structure and landscape pattern through indices is relatively widespread in landscape ecology and landscape planning. The lack of comparability of the results between different case studies and across spatial resolutions limits the potential usefulness of landscape metrics, in a context where multi-scale GIS and high resolution remotely sensed data are becoming increasingly available. In this paper, an object-based methodology to analyse and quantify connectivity at a landscape level as a general measure is described. Connectivity is to some degree species-dependent.

Segmentation algorithms have already been recognized as a valuable and complementary approach that similar to human operators perform a region-based rather than a point-based evaluation of high-resolution and multi-source remotely sensed data.

Multispectral classification, one of the most commonly used methods for mapping burned areas, is based on the spectral properties of different classes of interest and employs special algorithms designed to perform various types of spectral analysis. However, the use of these classifications has been repeatedly reported to create confusion between burned areas and nonvegetation categories, especially water bodies and shaded areas. As a result of the aforementioned, spectral based classification methods cannot be used operationally for the mapping of burned areas from satellite images.