CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: CURVELET APPROACH FOR SAR IMAGE DENOISING, STRUCTURE ENHANCEMENT, AND CHANGE DETECTION Andreas Schmitt, Birgit Wessel, Achim Roth

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 152 combination of wavelets and curvelets. A total variation based segmentation algorithm divides the image in structured regions, that are subsequently denoised by a curvelet-based method, and homogeneous regions, denoised by a wavelet approach. For large scenes with different land cover types, this method seems to be very promising. As we concentrate on urban applications in this paper, we use a purely curvelet-based approach. Change detection in SAR images being a very difficult task has often been discussed in literature. An overview to principal SAR change detection methods, their advantages as well as their dis advantages can be found in (Polidori et al., 1995). Some more specialized methods are touched in the following. The approach of (Balz, 2004) uses a high resolution elevation model (e.g. ac quired by airborne laserscanning) to simulate a SAR image which is subsequently compared to the real SAR data. The quality of the results is naturally highly dependent on the resolution of the digi tal elevation model and its co-registration to the SAR image. This nontrivial co-registration constraints this approach to small scale exemplary applications. Another idea starting with the fusion of several SAR images of different incidence angles to a ’’superreso- lution” image is presented by (Marcos et al., 2006) and (Romero et al., 2006). Man-made objects, i.e. geometrical particularities that are not captured by the digital terrain model used for the or thorectification of the SAR image, are classified by their diverse appearance in the single orthorectified images due to the different acquisition geometries. So, seasonal changes in natural surround ings can easily be distinguished from changes in built-up areas. One disadvantage is the large number of different SAR images of the same area needed to generate the ’’superresolution” image. (Wright et al., 2005) exploits the coherence (phase information) of two SAR images, which implies a relatively short repeat-pass time to avoid additional incoherence caused by natural surfaces. (Derrode et al., 2003) and (Bouyahia et al., 2008) adopt a hidden and a sliding hidden Markov chain model respectively to select areas with changes in reflectivity even from images with differ ent incidence angles. Although this method allows to process very large images and does not need additional parameter tun ing, except the window size, according to the authors still a lot of research work has to be done to improve the preliminary results. 3 CURVELET REPRESENTATION The curvelet representation consists of three components accord ing to (Candes and Donoho, 1999): Ridgelets These two dimensional waveforms are the basic ele ments of the curvelet representation. In the spatial domain, they appear like a ridge or a needle (see Fig. 1); in the curvelet domain their contribution to the original image is (a) Spatial domain (b) Curvelet coefficients Figure 2: City center of Munich, imaged by TerraSAR-X, High Resolution Spotlight mode, Polarisation VV, Spatially Enhanced Multi Look Ground Range Detected product measured by a coefficient. The magnitudes of the ridgelets extracted from Fig. 2(a) are depicted in Fig. 2(b) by gray- values. Bright pixels mark high magnitudes. In contrast to wavelets, curvelets are additionally defined by their ori entation in the two dimensional space (Ying et al., 2005). Hence, this is a method of image analysis suitable for image features with discontinuities across straight lines. Multiscale ridgelets As the decomposition into ridgelets is de pendent on the scale, a pyramid of windowed ridgelets is used, renormalized and transported to a wide range of scales and locations. For example, a ridgelet on the finest scale (N4-neighborhood) can only be horizontally or vertically oriented, i.e. two different orientations, while a ridgelet on the next coarser scale has already twice as much, i.e. four different orientations. Consequently, the resolution in ori entation increases with coarser ridgelet scales. The number of directions is given by the formula 2 subband . For redun dancy reduction a wavelet decomposition is commonly used on the finest scale, where only horizontal and vertical direc tions are discriminable anyway (Candes et al., 2005). The different scales appear in Fig. 2(b) as single rings, whereas the outer rings show the finer scales. The gaps between the rings are just for visualization.

Access restriction

Copyright

fullscreen: CMRT09

Monograph

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation