Extracting Buildings by Using the Generalized Multi Directional Discrete Radon Transform

This paper presents a new method to detect and accurately locate a rectangular form object in any given image. In order to find the right coordinates of those objects in the image, we develop the Generalized Multi Directional Discrete Radon Transform (GMDRT). The GMDRT can detect any given shape whatever its form and orientation are. Experimental results on high resolution QuickBird image to extract rectangular buildings form show the efficiency of our method.

[1]  Enrico Magli,et al.  Pattern recognition by means of the Radon transform and the continuous wavelet transform , 1999, Signal Process..

[2]  S. Mukhopadhyay,et al.  An edge preserving noise smoothing technique using multiscale morphology , 2002, Signal Process..

[3]  Ying Hao,et al.  Radon Transform and Forstner Operator Applying in Buildings Contour Extraction , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[4]  Gregory Beylkin,et al.  Discrete radon transform , 1987, IEEE Trans. Acoust. Speech Signal Process..

[5]  Ph. Courmontagne An improvement of ship wake detection based on the radon transform , 2005, Signal Process..

[6]  Amine Naït-Ali,et al.  Generalized multi-directional discrete Radon transform , 2013, Signal Process..

[7]  Dong-Chen He,et al.  Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge , 2010 .

[8]  Dong-Chen He,et al.  A new approach to building identification from very‐high‐spatial‐resolution images , 2009 .

[9]  J. Kruskal,et al.  COMPUTERIZED TOMOGRAPHY: THE NEW MEDICAL X-RAY TECHNOLOGY , 1978 .

[10]  Hmida Rojbani,et al.  Rθ-signature: A new signature based on Radon Transform and its application in buildings extraction , 2011, 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[11]  P. Subashini,et al.  An Optimal Method For Wake Detection In SAR Images Using Radon Transformation Combined With Wavelet Filters , 2009, ArXiv.

[12]  Qiaoping Zhang,et al.  Accurate Centerline Detection and Line Width Estimation of Thick Lines Using the Radon Transform , 2007, IEEE Transactions on Image Processing.

[13]  Wei Zhang,et al.  Multi-Scale Blur Estimation and Edge Type Classification for Scene Analysis , 1997, International Journal of Computer Vision.

[14]  Peyman Milanfar A model of the effect of image motion in the Radon transform domain , 1999, IEEE Trans. Image Process..