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Vision based 3D city modelling by using airborne laser scanner data for urban GIS
Buildings in Japan
Many Japanese buildings and houses are planned based on a unit of approximately 2m x 2m in the horizontal directions and the building height is 2m/floor [7]. Therefore the present airborne Laser scanner data (Airborne Helicopter; 200-400m aMSL; 2000/sec pulse length; 20/sec scan Time; 50cm x 50cm resolution) is expected to offer enough capability of detecting and modelling buildings in Japan. Thus the ALS data has sufficient spatial accuracy and resolution (Fig.2). The ALS system is capable of giving orthoimage by combining the position and altitude of ALS and a CCD array sensor mounted on the same platform (Fig.3). 
Data Processing Building data are detected by thresholding the DSM data. The threshold is chosen according to prior knowledge about the buildings. Since the interpretation based solely on range/height data is difficult, a colour air-photo (visible or infrared band) co-registered with the laser measurements has been utilised interpretation (Fig.3). A histogram-based data scaling has been carried out after the interpretation of colour air-photo of corresponding ALS image (Fig. 2). Smoothing by Modified SUSAN filter In the Smoothing over Univalue Segment Assimilating (SUSAN) noise filtering algorithm a Gaussian in the brightness domain has been employed for smoothing [9]. This means that the SUSAN filter is like the sigma filter in the brightness and the spatial domains and the Gaussian filter in the spatial domain. Since laser data is elevation data with more speckle noise, it is decide to use median filter in the brightness domain [10]. The SUSAN filter works by taking an average over all of the pixels in the locality that lie in the USAN. It is obvious that this will give the maximal number of suitable neighbours with which to take an average, whilst not involving any neighbours from unrelated regions. Thus all image structure should be preserved. 
Edge detection The pixel values in the laser data represent elevation and thus, the zero-crossings in the convolution output denote significant elevation changes [2]. For example, the positive values (or positive valued regions) in the convolution output represent the objects above the datum. Lapalacian of Gaussian (LoG) edge detector has been used for edge detection in the laser data in the present research.  Fig. 5: Results of SUSAN filter The LoG operator calculates the second spatial derivative of an image. This means that in areas where the image has a constant intensity (i.e. where the intensity gradient is zero), the LoG response will be zero. In the vicinity of a change in intensity, however, the LoG response will be positive on the darker side, and negative on the lighter side. LoG has the following advantages for extraction of buildings from laser data: Building edges are step edge and LoG is good for step edges. It generates closed edges, which avoids linking problems faced by some other edge detectors (Since all building edge is closed edge, closure property can also be used to get ride some non-building edges). It is insensitive to noise. The strength value of each LoG edge can be used to remove some noise edges, assuming building edges are strong edges.  Fig. 6: Results of edge detectors Figure 6 illustrates the effects of LoG on ALS images. It was observed that the LoG output shows closed and thin boundaries of buildings. Edge Thinning and Vectorisation The thinning method, utilising mathematical algorithm of Zhang Suen Thinning has produced appreciable results [11]. The Zhang Suen method tends to be better at extracting straight lines from a raster and resulted in more desirable vectors from an edge image, which comprises mainly straight lines. The fast algorithm presented here is able to separate different cells on the basis of intensity information and to measure their geometrical properties.  Fig. 7: Thinning effect Once a raster has been thinned down to lines of single width pixels, and then vectorisation can be used to extract real vectors. The designed method converted images that contain inconsistent line weights and filled areas. This method converted images by tracing an outline of each element in the image. Figure 7 shows polygons of building boundaries after thinning process. |