and geodata

Dpartement de mathmatiques

This paper presents a multi-resolution approach for automatic extraction of roads from digital aerial imagery. Roads are modeled as a network of intersections and links between the intersections. For different context regions, i.e., rural, forest, and urban areas, the model describes different relations between background objects, e.g., buildings or trees, and semantic road objects, e.g., road-parts, road-segments, road-links, and intersections. The classification of the image into context regions is done by texture analysis. The approach to detect roads is based on the extraction of edges in a high resolution image and the extraction of lines in an image of reduced resolution. Using both resolution levels and explicit knowledge about roads, hypotheses for roadsides are generated. The roadsides are used to construct quadrilaterals representing road-parts and polygons representing intersections. Neighboring road-parts are chained to road-segments. Road-links, i.e., the roads between two...

Aerial imagery is an important source for the acquisition and update of GIS data. By using digital imagery it is possible to automate some parts of these tasks. In this context this paper proposes a new approach for the automatic update, i.e., verification and extraction, of roads from aerial imagery. The verification process evaluates road axes from GIS data based on the analysis of profiles taken perpendicularly to the axes. It is possible to handle inaccurate axes, as well as to detect initial points for branching roads. The process for the extraction of roads is independent of the GIS data, but relies on knowledge about roads provided by a road model. This model comprises knowledge about geometrical, radiometrical, topological, and contextual properties of roads at different resolutions. Multi-resolution extraction is applied because distinct characteristics of roads can be detected best at different resolution levels. By fusing results of different resolution levels the distinct c...

For quickly populating GIS database, it is important to derive accurate and truly road information from imagery. In this paper, we describe the problem of urban road extraction from digital imagery using adaptive active contour models (Snakes). Our road extraction processing has three steps. First, we segment the image based on the dominant road directions. Second, we detect the road lines with the so called ‘acupuncture method ’. Finally, we refine the road edges by applying adaptive snakes to the corner desired approximation to extract the city block. During the process, we assume that the road network and block pattern in the city have a semi-regular grid pattern. For detecting the road lines, we exploit the distribution of edges in an urban area. Linear associated with roads are detected and these become the basis for initial approximations to road grid pattern for snakes based refinement. In order to accommodate variable line characteristics, we have developed an adaptive algorithm which locally modifies the weight of the energy terms. These ideas are applied to same actual urban imagery and the results are displayed and evaluated. 1.

This paper describes the state of the art in segmentation algorithms of aerial images. Different approaches and object classes are described and their advantages and limitations are shown. First the advantage of multiple input data (e.g., color, infrared, DEM) and the information that can be derived from these sources is discussed. Besides sensor data, "synthetic" input images (e.g., using texture filters) are generated to support the segmentation process. After an optional noise cleaning, primitives are extracted in scale space. This offers the possibility of selecting an optimal resolution depending on the size and shape of an object. Using this resolution, the raw segmentation will be stable and conflicts with other object classes will be reduced. Depending on the class of the object the final extraction has to be selected: Compact artificial objects can be segmented using primitives like areas, lines, or points. Linear objects like roads are similar but the borders are curves and the size is not limited. Arbitrary areas like meadows, forests, or fields have an arbitrary border and are mainly defined by their specific texture. Objects like trees or cars have to be treated in a very specific manner. Finally, different base algorithms for segmentation are discussed: Pixel classification is very simple but lacks the use of context. The extraction of primitives (egdes, lines, area, points) can be used as a basis for a wide class of objects. Texture analysis can be used for a rough segmentation of the image. Specialized operations are useful for the extraction of objects like single trees or to support the interpretation process.

Extracting roads from aerial imagery is important for the update of Geographic Information Systems (GIS), e.g., to capture new roads or to improve the geometric accuracy of existing roads. It can be broken down into two phases: First, old data is verified to determine which parts of the data have changed. Then, newly built roads are added. In this paper a two-phase approach consisting of automatic verification, as well as automatic road extraction is proposed. In the verification phase roads are searched for in the image along the axes given by the old data. This is done by using the gradient information of the image to localise possible roadsides. For the extraction of new roads a two-resolution approach with successive fusion has been developed. In a reduced version of the image center lines of roads are extracted, while in the original resolution roadsides are detected. The results of the two resolution levels are fused with a number of rules. Results of the proposed approach on aer...

Information about current land-cover in forests is important for management and conservation of these areas. Up to the last decade traditional per pixel classification algorithms were used to be utilized in extracting land-cover information. However, they are poorly equipped to monitor land-cover in images acquired by current generation of satellite sensors with adequate accuracy. A good understanding and classification of an image can be done by gathering critical a priory knowledge about the study area and an effective use of channels involved in the procedure. It is important to make use additional spectral and spatial knowledge in order to improve the classification accuracy. In this study, a knowledge based hierarchical approach is proposed in order to classify and detect forest types in the Ömerli Dam Lake Region. The method makes use of the fact that land-cover types and their associated knowledge form a natural hierarchy. Hierarchical classification is a powerful approach in solving classification problems by decomposing the image into a hierarchical tree structure. This also results in sub-dividing the area into spectrally consistent regions and helps dealing with spectral variability within each subarea. Three types of knowledge were involved in the rule-based classification of the study area: Domain spectral knowledge, Spectral classification rules obtained from training data and Spatial knowledge. Sub-dividing the area into smaller homogeneous regions in hierarchical classification increased the accuracy, while supervised classification technique yielded 47 per cent in the same area. Spatial reclassification involved in the hierarchical classification method increased overall accuracy, yielding new classes like coast. 1.1 Aim of the Study 1.

roads from dense point clouds in large scale

Abstract not found