Genauigkeitsschätzung digitaler Höhenmodelle mittels Spektralanalyse

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Finding Spatial Units for Land Use Classification Based on Hierarchical Image Objects

Remote sensing in urban areas has been a challenger for quite some time due to their complexity and fragment with combination of man-made features and natural features. High-resolution satellite images and airborne laser altimetry data offered potential possibilities for feature extraction and spatial modelling in urban areas. Land use classification of urban areas may become possible by exploiting current high-resolution sensor data. The proposed approach incorporates spectral information from multi-spectral IKONOS images and height information from laser scanning data in hierarchical image segmentation based on semantically meaningful thresholds. By image segmentation, we obtain image objects at several levels with certain properties, which make it possible to include the spatial relations between adjacent image objects. Land cover classification and identification of image objects can be carried out mainly according to their properties. Land use classification at a higher level need to be inferred based on land cover objects and structural information at lower levels. We use Delaunay triangulation for deriving spatial relations between image objects and for structural analysis. Based on adjacency relationships of image objects, human settlements and other urban spaces are formed that create a base for land use identification as well as for structural analysis of urban areas. In this paper, the hierarchical image segmentation schema and the corresponding semantic-based thresholds are presented. To test the approach, we selected a site in a suburban area in Amsterdam, the Netherlands. The experiments show that hierarchically formed image objects are useful tools for image analysis and spatial modelling as compared to pixel-based approaches. Structural information ca..

Hierarchical image object-based structural analysis toward urban land use classification using high-resolution imagery and airborne LIDAR data

High-resolution remotely sensed imagery and airborne laser altimetry data offer exciting possibilities for feature extraction and spatial modelling in urban areas. In this study, hierarchical image objects have been generated by image segmentation based on IKONOS imagery and laser scanning data using semantically meaningful thresholds. Delaunay triangulation and morphological image analysis technique have been applied in deriving spatial relations between image objects and for structural analysis. Land use objects can be inferred at a higher level based on land cover objects and structural information. In this paper, an overview is given of an urban land use classification schema based on hierarchical image objects. The image objects at each level are described, their spatial properties mentioned and the derivation of structural information is outlined. The focus of this paper is on the higher level of spatial clusters and spatial units: land use functions through structural analysis of related features, spatial relations and associated measurements. Finding discriminant functions for identifying land use and its spatial units is a major concern. A number of measurements are introduced and experimental results are compared and evaluated. These experiments are based on different types of data from a study area in southeast Amsterdam

Building Extraction From Laser Data By Reasoning on Image Segements in Elevation Slices

Remote sensing in urban areas has been a challenge for quite some time due to complexity and fragmentation of objects and the combination of man-made features and natural features. Airborne laser altimetry data offer possibilities for feature extraction and spatial modelling in urban areas. There are many approaches of deriving buildings and other features currently available in literatures. However, there are many cases, which are still difficult for particular features to be extracted by using these approaches. For instance, in an urban area where many roads are raised above ground level with special characters similar to buildings. Building extraction in such a complicated urban context is still a difficult task for these available approaches. The proposed approach was developed to solve this type of cases. It tries to extract buildings through reasoning in a layer space in general. In the proposed approach, airborne laser altimetry data in raster format was segmented by using several thresholds with 1-meter interval of altitude. These image segments were then labelled and assigned with unique label values, which are treated as image objects. Hence, a number of properties can be derived based on labelled segments (image objects) such as size, shape, orientation etc. These properties are used for reasoning in the layer space. The layer space is defined as such that use altitude with 1-meter interval as a variable in X-axis and use these properties as functions of altitude in Y-axis. Vertically segmented image objects are linked and inferred vertically as well. A tree structure was created using links between different layers of segments vertically. Reasoning is based on patterns of these properties on the paths of each branch of searching tree in the layer space. S..