Framelet transform based edge detection for straight line detection from remote sensing images


  • Vidhya Rangasamy
  • Sulochana Subramaniam Anna University


Discrete wavelet transforms (DWT), principal component analysis (PCA), singular value decomposition (SVD).


Edge detection has been widely used as a pre-processing step for image processing applications such as region segmentation, feature extraction and object boundary description. Classical edge detection operators available in literature are easy to implement, but not all the edge detection operators is suitable for remote sensing images in terms of
selecting threshold and kernel function. There is no acceptable method to select the parameters in classical edge detection methods. Multiresolution analysis such as wavelet transform has been shown to have advantages over classical edge detection techniques, as it is less sensitive to noise. The discrete wavelet transform (DWT) is shift variant, due to critical subsampling. The DWT is not capable of capturing edges, which are not aligned in horizontal and vertical directions. In this paper, we focus beyond DWT, framelet transform used to detect edges from LISS III and Cartosat images. The proficiency of the proposed method is evaluated by comparing the results of DWT, dual tree complex wavelet transform
(DTCWT), curvelet transform (CUT), contourlet transform (CT) and non subsampled contourlet transform (NSCT) based edge detection methods. Rosenfeld evaluation metric is used to measure the quality of the edge detection methods, which shows the framelet based edge detection produce sound results than other methods. Principal component analysis (PCA) and singular value decomposition (SVD) methods are used to remove the correlation among the multispectral bands and selected maximum information bands for edge detection, instead of using one particular band because each band in multispectral image is suitable for specific applications. The detected edges are further subjected to line
detection algorithms such as standard Hough transform, small eigenvalue analysis and principal component analysis. The outcomes are compared in terms of complexity measurements. Framelet transform along with principal component analysis based line detection algorithm perform better than other two methods.

Author Biography

Sulochana Subramaniam, Anna University

Institute of Remote Sensing, Anna University,Chennai,India


Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 679-698.

Candes, E.J. & Donoho, D.L. (2000). Curvelets a surprisingly

effective nonadaptive representation for objects with edges. Vanderbilt University Press, Pp. 1-10.

Cunha, A.L., Zhou, J.P & Do, M.N. (2006). The nonsubsampled

contourlet transform: Theory, Design, and Applications. IEEE

Transactions on Image Processing, 15(10):3089-3101

Duda, R.O. & Hart, P.E. (1972). Use of hough transformation to

detect lines and curves in Pictures. Communications of the ACM,


Do, M.N. & Vetterli, M. (2003). Contourlets in beyond wavelets. G.V. Welland, Ed. New York: Academic Press. Do, M.N. & Vetterli, M. (2005). The contourlet transform: An efficient

directional multiresolution image representation. IEEE Transactions on Image Processing, 14(2):2091-2106.

Golub, G.H. & Loan, C.F.V. (1996). Matrix computations. The Johns

Hopkins University Press, 3rd edition.

Guru, D.S. Shekar, B.H. & Nagabhusan, P. (2003). A simple and

robust line detection algorithm based on small eigenvalue analysis.

Pattern Recognition Letters, 25(1):1-13.

Gonzalez, R.C. & Woods, R.E. (2002). Digital imaging processing.

Second Edition, Pearson Education, ISBN: 81-7808-629-8.

Hadeel, N. & Taai, A.I. (2008). A novel fast computing method for

framelet coefficients. American Journal of Applied Sciences, 1522-1527.

Jolliffe, I. (2002). Principal component analysis. Springer, 2nd Edition.

Kitchen, L. & Rosenfeld, A. (1981). Edge evaluation using local

edge coherence. IEEE Transactions on Systems, Man and Cybernetics, 11(9):597-605.

Lee, S.E, Koo, H.S. & Jeong, C.S. (2006). A straight line detection

using principal component analysis. Pattern Recognition Letters,


Marr, D. & Hildreth, E. (1980). Theory of edge detection. Proceedings of Royal Society of London B, 187217.

Meer, P. & Georgescu, B. (2001). Edge detection with embedded

confidence. IEEE Transactions on Pattern Analysis and Machine

Intelligence, 23(12):1351-1365.

Mohsin, M.K. (2013). No Reference image quality assessment

depending on YCbCr and L*u*v. European Scientific Journal,

December 2013 - SPECIAL Edition - vol. 3.

Nevatia, R. & Ramesh Babu. (1980). Linear feature extraction and

description. Computer Graphics and Image Processing, 13:257- 269.

Rothwell, C. Mundy, J. Hoffman, B. & Nguyen, V.D. (1995). Driving

vision by topology. International Symposium, Computer Vision, Pp.


Smith, S.M. Brady, J.M. (1995). A new approach to low level image

processing. International Journal of Computer vision, 23(1):45-78.

Soman, K.P. Resmi, N.G. & Ramachandran, K.I. (2010). Insight

into wavelets: from theory to practice, Pp. 1-75.

Selesnick, I.W. Baraniuk, R.G. Kingsbury, N.G. (2005). The dual

tree complex wavelet transform. IEEE Signal Processing Magazine,


Selesnick, I.W. & Abdelnour, A.F. (2004). Symmetric wavelet tight

frame with two generators. Applied and Computational Harmonic

Analysis, 17:211-225.

Selesnick, I.W. (2004). A higher density discrete wavelet transform. IEEE Transactions on Signal Processing, 54(8):3039-3048.

Zhou Wang., Sheikh, H.R. & Bovik, A.C. (2002). No-reference

perceptual quality assessment of JPEG compressed images. IEEE

International Conference on Image Processing, 1:1477-1480.