CO-REGISTERING AND NORMALIZING STEREO-BASED ELEVATION DATA TO SUPPORT BUILDING DETECTION IN VHR IMAGES

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1 CO-REGISTERING AND NORMALIZING STEREO-BASED ELEVATION DATA TO SUPPORT BUILDING DETECTION IN VHR IMAGES Alaeldin Suliman, Yun Zhang, Raid Al-Tahir Department of Geodesy and Geomatics Engineering, University of New Brunswick 15 Dineen Drive, Fredericton, New Brunswick, Canada, E3B 5A3 ABSTRACT Building detection from very high resolution (VHR) remote sensing images has been an active research area for more than two decades. This is because building information is crucial for analysing urban environments, and also because VHR images are the ideal geo-spatial data source for extracting and mapping such information. Since optical images are a 2D projection of the real world surface, elevation data provide an additional key component for detecting and delineating buildings. Therefore, optical images and elevation datasets must be accurately co-registered in order to detect and map buildings. This is a challenging task by itself. Additionally, the co-registered elevations need to be normalized (i.e. removing the terrain elevation) so that elevation values only represent the heights of aboveground objects. The normalization process often introduces elevation errors and reduces the building detection quality. To remedy this problem, this paper introduces an integrated method for co-registration and terrain elevation filtration to directly generate a set of 3D points that facilitate detecting elevated buildings in a 2D image. An image-space co-registration of a dense set of image matching points is proposed. These points are identified and matched in a pair of stereo VHR images and then assigned their corresponding elevations generated by photogrammetric/aerial triangulation. Once an accurate image-space registration of the triangulated elevations is achieved, an image classification technique is executed to identify the terrainlevel natural and artificial objects (e.g., grass and roads). The associated terrain-level elevations are then extracted and utilized in the normalization process that allows a direct elevation-based building detection. The proposed method was executed and validated. Improved building detection results over traditional methods were achieved. Key words: Building detection, very high resolution (VHR) images, optical-elevation data coregistration, and elevation data normalization. 1. INTRODUCTION Very high resolution (VHR) images are characterized by broad coverage, fast acquisition, and relatively inexpensive prices. This makes such images the ideal geo-data for mapping building information (Salehi et al., 2011). Buildings are one of the most important classes in urban mapping. The current building distribution and development in a city are essential information for urban environmental analysis and planning (Gruen and Nevatia, 1998; Zhang, 1999). As a result, building detection in remote sensing VHR images has been an active area of research within the remote sensing community (Awrangjeb et al., 2011; Khosravi et al., 2014; Doxani et al., 2015). Since buildings are elevated objects, building heights play an important role in the reliability of the detection process. Therefore, the co-registration of the elevation data with optical VHR images is a necessity to implement elevation-based building detection. However, several problems are introduced when such datasets are integrated (Dowman, 2004). The misregistration between these different data sources is one of the most critical problems. This type of problem is severe in off-nadir VHR images

2 especially when the conventional techniques of 2D image-to-image registration are used (Suliman and Zhang, 2015). Photogrammetric approaches and LiDAR (light detection and ranging) technologies are the most commonly used to generate elevation data. Both of these technologies provide the height information at the tops of surfaces such as buildings or trees. Hence, they result in digital surface models (DSMs). However, only the aboveground (normalized) building heights are required for all elevation-base building detection methods. These normalized heights require normally the extraction of the digital terrain model (DTMs) and then subtraction from its corresponding DSM. Unfortunately, extracting the DTMs from DSMs is still a problem to some extent (Sithole and Vosselman, 2004; Zhang et al., 2004). Based on the conducted literature review, most of the elevation-based detection methods encounter the above mentioned two challenges: the optical-elevation data co-registration and aboveground height calculation. Therefore, in this paper we are introducing a building detection method that attempts to overcome these two challenges. This paper is organized as follows: the proposed method is described in Section 2, the data and the achieved results are presented and discussed in Section 3, and finally, the conclusions are drawn in Section THE PROPOSED METHOD In this study, we propose the use of tie points with their photogrammetrically-triangulated elevations to achieve accurate and simple optical-elevation data co-registration. For the aboveground height calculation, the DTM of the study area is proposed to be interpolated from the co-registered elevations that lie within terrain level land-cover objects. Since these terrain-level points are already co-registered in the image space, they can be identified easily by executing an image classification technique. The generated DTM is then subtracted from its corresponding DSM to calculate the above ground heights. The phases of the proposed method are flowcharted in Figure 1. This flowchart illustrates four phases to achieve building detection based on stereo elevation information. The proposed method starts by processing the input image by pixel-based classification and image segmentation. The second phase is calculating the height information based on the stereo images. In the third phase, the aboveground heights are calculated. Finally, the building objects are mapped and finished in the fourth phase. Figure 1: Flowchart of the proposed method

3 3. DATASET AND RESULTS 3.1 Dataset Used The dataset used in this study comprises three stereo VHR airborne images captured over the center plaza of Overland Park, KS, USA. Each of these images covers a ground area of 1Km by 0.75Km with a ground sampling distance (GSD) of 0.25m. The sensor information and the acquisition geometry are provided with the image data. The test image used in this study is shown in Figure 2. This image contains areas of different land-cover and land-use types. The urban area in the images includes buildings of different scales as well as traffic areas, i.e. artificial (man-made) features that lie on terrain or off-terrain levels. On the other hand, terrain level natural features include soil lands, grass lands, and water bodies. Figure 2: The test image data used in the study. 3.2 Achieved Result After implementing the phases as developed in Section 2, the following figures illustrate the result achieved. An example of the optical-elevation data co-registration is presented in Figure 3. After image segmentation, Figure 3 also shows an image segment along with its representative point (RP) which in this case the inner-centroid point. To guarantee that this RP is located inside the segment boundary, if the calculated centroid is located outside its segment, this RP is replaced by the center of the greatest circle that fit inside the segment borders. Figure 3: An example of co-registered optical-elevation data along with the representative (centroid) point of an image segment. The distribution of the matching points that are co-registered with the optical image is illustrated in Figure 4. The point distribution was arraigned in grid form as shown in Figure 4-(a). Based on the result of image classification performed in the first phase, the co-registered matching points were then categorized into terrain level natural class points (Figure 4-(b)) and off-terrain and artificial features class points (Figure 4-(c)).

4 (a) (b) (c) Figure 4: Point categorization based on spectral classification information: (a) grid distribution of the generated matching points in the test image; (b) matching points lie on terrain segments; (c) points lie on off-terrain and artificial segments. Once the terrain-level points have been detected, surface interpolation was implemented. In the same manner, the DSM in the image-space, based on the co-registered elevations of the matching points, was generated by surface interpolation. By subtracting these two models in the image space, the aboveground/normalized heights were calculated for all segments of the terrain and off-terrain artificial features at the locations of the segment RPs. After that, a threshold was applied to detect building roofs. Figure 5 shows the detected RPs of the building segments in the test image. The final detection result is illustrated in Figure 6. Figure 5: The detected points on top of the building roofs shown as green dots in the test image Figure 6 illustrates the detected building objects based on stereo elevations. Many man-made groundlevel objects of very similar spectral information to the building roofs were removed from the detected urban objects. The result was accurate and reliable because the key component used for building detection was the elevation. Figure 6: The detected building roofs and traffic areas.

5 4. CONCLUSIONS This paper proposes a method for building detection based on elevation data. Elevation points were successfully co-registered to the VHR image by utilizing image-space matching points and their triangulated ground elevation to achieve accurate optical-elevation data co-registration in the image space. Additionally, the terrain elevations were filtered out based on image classification information to calculate the aboveground heights of the buildings in the test area. After implementation, the developed elevation-based building detection method was able to provide successful optical-elevation co-registration and robust aboveground height extraction. Moreover, this method was able to distinguish reliably the building roofs from their spectrally-similar ground level objects. The developed method did not only improve the quality of the elevation-based building detection, but also facilitated the incorporation of the elevation data through straightforward co-registration and elevation normalization. Future research will focus on the challenges associated with more complex urban environments. ACKNOWLEDGEMENT This research is funded in part by the Libyan Ministry of Higher Education and Research (LMHEAR) and in part by the Canada Chair Research (CRC) program. The authors would like to acknowledge PCI Geomatics for providing the tutorial test data used in this research. REFERENCES Awrangjeb, M., Z. Chunsun, and C.S. Fraser, Automatic reconstruction of building roofs using LIDAR and multispectral imagery, Proceedings of the Digital Image Computing Techniques and Applications (DICTA), 2011 International Conference on, 6-8 Dec , pp Dowman, I., Integration of LIDAR and IFSAR for mapping, International Archives of Photogrammetry and Remote Sensing, 35(B2): Doxani, G., K. Karantzalos, and M. Tsakiri-Strati, Object-based building change detection from a single multispectral image and pre-existing geospatial information, Photogrammetric Engineering and Remote Sensing, 81(6): Gruen, A., and R. Nevatia, Automatic building extraction from aerial images, Computer Vision and Image Understanding, 72(2): Khosravi, I., M. Momeni, and M. Rahnemoonfar, Performance evaluation of object-based and pixel-based building detection algorithms from very high spatial resolution imagery, Photogrammetric Engineering and Remote Sensing, 80(6): Salehi, B., Y. Zhang, and M. Zhong, Object-based land cover classification of urban areas using VHR imagery and photogrammetrically-derived DSM, Proc. of ASPRS 2011 Annu. Conf., Milwaukee, WI Sithole, G., and G. Vosselman, Experimental comparison of filter algorithms for bare-earth extraction from airborne laser scanning point clouds, ISPRS Journal of Photogrammetry and Remote Sensing, 59(1):

6 Suliman, A., and Y. Zhang, Development of line-of-sight digital surface model for co-registering off-nadir VHR satellite imagery with elevation data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5): Zhang, Y., Optimisation of building detection in satellite images by combining multispectral classification and texture filtering, ISPRS Journal of Photogrammetry and Remote Sensing, 54(1): Zhang, Y., C.V. Tao, and J.B. Mercer, An initial study on automatic reconstruction of ground DEMs from airborne IfSAR DSMs, Photogrammetric Engineering & Remote Sensing, 70(4):

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