New data resources from high spatial resolution satellite sensors like Ikonos or Quickbird, and innovative concepts in image analysis have the potential for improving mapping and analysis of urban land use structures and related dynamics. Considering the high amount of spatial detail in those types of data and the land cover heterogeneity of urban areas it is more difficult to apply traditional digital image analysis algorithms to derive thematic information.

Germany’s aerospace research center and space agency with 4,700 employees in 35 research units at 8 sites.
Major competence fields: Remote sensing, Earth observation, Communication and Navigation, Robotics, Space research with manned and unmanned missions.

Region-based approaches are suitable for the analysis of high-resolution remotely sensed data. In this context, special emphasis has to be laid on the segmentation of multisensoral and multi-scale input data with respect to the geometrical and semantical robustness of the achieved results, but also on the grade of automatization and transparency of the algorithms.

Sustainable Development as a concept is generally widely accepted. The crucial issue is its operationalisation. This paper starts from the assumption that both words are intuitively tight together implying that there is “development” and consequently “change”. Therefore it is hypothesised that these changes will leave footprints on the earth’s surface and we still will be able to detect and measure subsequently the resulting “manifestations” within the environment.

Definiens is a new innovation oriented German company which developed a powerful image analysis software, eCognition. One of the current research projects within the company is to expand the capabilities of eCognition for SAR information extraction. These developments will help remote sensing on its way from a research oriented to an operational technology with commercial benefit. To support this development, the German Aerospace Center (DLR) co-funds the project ProSmart II and therein the sub-project OSCAR (object oriented segmentation and classification of advanced SAR data).

A necessary prerequisite for object oriented image processing is successful image segmentation. The approach presented in this paper aims for an universal high-quality solution applicable and adaptable to many problems and data types. As each image analysis problem deals with structures of a certain spatial scale, the average image objects size must be free adaptable to the scale of interest.

Ziel der Diplomarbeit war die Entwicklung einer Methode zur Ableitung von Volumsklassen und zur Bestimmung der Baumartenzusammensetzung von Waldbeständen aus Landsat 7- Daten in einem borealen Waldgebiet Sibiriens. Von vier jeweils über 20.000 ha großen Testflächen standen Referenzdaten der Forstinventur zur Verfügung. Durch eine Signaturanalyse wurden die Zusammenhänge zwischen den Bestandesparametern und den spektralen Merkmalen untersucht.

In the framework of a European Union project a method is needed that allows rapid, standardized, and comparable assessment of the recent state of biodiversity in several countries at the same time. Landscape ecological research in the last decades showed that diversity of species can be correlated to the diversity of their habitats, and that remote sensing images provide good basis for this assessment. Traditional image processing methods ordered pixels of the image into classes according to the habitat they represent.

Remotely sensed imageries are an important source for generating and updating GIS databases for land use and land cover change detection in urban areas. The interpretation of these data is a complex task because of the high variability of the image material under investigation. The purpose of this project was to compare the traditional classification approaches like the maximum likelihood method with an alternative object-oriented method. For this project Landsat ETM+ image with a ground resolution of 30-meter was used.

This paper describes a procedure for a multitemporal interpretation of vegetation areas in Brazil using Landsat TM images. We started with a monotemporal interpretation of the first epoch of the scene by using both spectral and structural features. For interpretation of temporal changes we discretely described temporal conditions of regions, and transferred the most probable temporal changes of the given conditions as temporal knowledge into a state transition diagram, which was used for the multitemporal interpretation of the images.