CURVELET APPROACH FOR SAR IMAGE DENOISING, STRUCTURE ENHANCEMENT, AND CHANGE DETECTION

In this paper we present an alternative method for SAR image denoising, structure enhancement, and change detection based on the curvelet transform. Curvelets can be denoted as a two dimensional further development of the well-known wavelets. The original image is decomposed into linear ridge-like structures, that appear in different scales (longer or shorter structures), directions (orientation of the structure) and locations. The influence of these single components on the original image is weighted by the corresponding coefficients. By means of these coefficients one has direct access to the linear structures present in the image. To suppress noise in a given SAR image weak structures indicated by low coefficients can be suppressed by setting the corresponding coefficients to zero. To enhance structures only coefficients in the scale of interest are preserved and all others are set to zero. Two same-sized images assumed even a change detection can be done in the curvelet coefficient domain. The curvelet coefficients of both images are differentiated and manipulated in order to enhance strong and to suppress small scale (pixel-wise) changes. After the inverse curvelet transform the resulting image contains only those structures, that have been chosen via the coefficient manipulation. Our approach is applied to TerraSAR-X High Resolution Spotlight images of the city of Munich. The curvelet transform turns out to be a powerful tool for image enhancement in fine-structured areas, whereas it fails in originally homogeneous areas like grassland. In the change detection context this method is very sensitive towards changes in structures instead of single pixel or large area changes. Therefore, for purely urban structures or construction sites this method provides excellent and robust results. While this approach runs without any interaction of an operator, the interpretation of the detected changes requires still much knowledge about the underlying objects.

[1]  T. Macklin,et al.  Coherent change detection with SAR , 2005, European Radar Conference, 2005. EURAD 2005..

[2]  E. Candès,et al.  Curvelets and Fourier Integral Operators , 2003 .

[3]  A. Lopes,et al.  A statistical and geometrical edge detector for SAR images , 1988 .

[4]  Mihai Datcu,et al.  Despeckling of TerraSAR-X Data Using Second-Generation Wavelets , 2010, IEEE Geoscience and Remote Sensing Letters.

[5]  Laurent Demanet,et al.  Fast Discrete Curvelet Transforms , 2006, Multiscale Model. Simul..

[6]  E. Candès,et al.  Continuous Curvelet Transform : I . Resolution of the Wavefront Set , 2003 .

[7]  E. Candès,et al.  Continuous curvelet transform: II. Discretization and frames , 2005 .

[8]  Stéphane Derrode,et al.  Change detection in synthetic aperture radar images with a sliding hidden Markov chain model , 2008 .

[9]  Emmanuel J. Candès,et al.  New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction , 2002, Signal Process..

[10]  Gregoire Mercier,et al.  Unsupervised change detection in SAR images using a multicomponent HMC model , 2003 .

[11]  David L. Donoho,et al.  Digital curvelet transform: strategy, implementation, and experiments , 2000, SPIE Defense + Commercial Sensing.

[12]  Timo Balz,et al.  SAR SIMULATION BASED CHANGE DETECTION WITH HIGH-RESOLUTION SAR IMAGES IN URBAN ENVIRONMENTS , 2004 .

[13]  Johannes R. Sveinsson,et al.  Combined wavelet and curvelet denoising of SAR images using TV segmentation , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[14]  Jesus Sanz-Marcos,et al.  SAR SUPERRESOLUTION CHANGE DETECTION FOR SECURITY APPLICATIONS , 2006 .

[15]  Juan Luis Valero,et al.  IMPLEMENTATION OF NEW SAR CHANGE DETECTION METHODS: SUPER- RESOLUTION SAR CHANGE DETECTOR , 2006 .

[16]  E. Candès,et al.  Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges , 2000 .

[17]  P. Lancaster.  Curve and surface fitting , 1986 .

[18]  E. Candès.  New tight frames of curvelets and optimal representations of objects with C² singularities , 2002 .

[19]  Naveed Sarfraz Khattak,et al.  Wavelet-Based Despeckling of Synthetic Aperture Radar Images Using Adaptive and Mean Filters , 2007 .

[20]  E. Candès,et al.  New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities , 2004 .

[21]  Liyong Ma,et al.  Pixel Fusion Based Curvelets and Wavelets Denoise Algorithm , 2007, Eng. Lett..

[22]  L. Polidori,et al.  Change detection in radar images: methods and operational constraints , 1995, 1995 International Geoscience and Remote Sensing Symposium, IGARSS '95. Quantitative Remote Sensing for Science and Applications.

[23]  E. Candès,et al.  Continuous curvelet transform , 2003 .

[25]  Lexing Ying,et al.  3D discrete curvelet transform , 2005, SPIE Optics + Photonics.