Pixel-based classifications have difficulty adequately or conveniently exploiting expert knowledge or contextual information. Object-based image-processing techniques overcome these difficulties by first segmenting the image into meaningful multipixel objects of various sizes, based on both spectral and spatial characteristics of groups of pixels. The segments (objects) are assigned classes using fuzzy logic and a hierarchical decision key. To date, the main drawback has been the lack of effective software.

Most traditional pixel-based classification approaches are based exclusively on the digital number of the pixel itself. Thereby only the spectral information is used for the classification. The situation comes into worse when extracting certain interested features only. As a result, it produces the salt-and-pepper-effect pseudocolor layer. An object-oriented classification is suggested in order to overcome the limitations stated above. The existing software, eCognition allows the polygon based classification process.

Multi-sensor remote sensing systems have been developed for a simultaneous acquisition of image and elevation data. However, reliable automatical processing methods – in particular for the interpretation of multi-sensoral data with high geometrical resolutions – are still at a development stage and by no means operational yet. In this context it will be shown how the increased potential of multi-sensor systems can be used to extract and process additional features for the purpose of urban landscape modelling.

It is in the interest of any gas company to maintain the value of its pipelines and to protect them effectively against damage caused by third parties. As a result of global progress in high-resolution remote sensing and image processing technology, it is now possible to design natural gas pipeline monitoring systems with remote sensors and context-oriented image processing software. Recent developments in UAV technology show their suitability as platforms for such customer driven missions [1,2]. Two different scenarios for a UAV based gas pipeline monitoring sys-tem will be discussed.

Using lidar point cloud data, the orientation and height of building roof faces can be estimated accurately but the outline of a roof face is more difficult to determine. To improve this part of the 3D building model reconstruction we make use of building footprints that are extracted from object-oriented classification of coincident high resolution imagery. In this process, the lidar derived DSM (Digital Surface Model) is used with high-resolution imagery for initial segmentation and subsequent classification.

In this paper, a new approach to road network extraction from multi-spectral (MS) imagery is presented. The proposed approach begins with an image segmentation using a spectral clustering algorithm. This step focuses on the exploitation of the spectral information for feature extraction. The road cluster(s) is automatically identified using a fuzzy classifier based on a set of predefined membership functions for road surfaces and the corresponding normalized digital numbers in each multi-spectral band.