Fast, Accurate and Consistent Modeling of Drainage and Surrounding Terrain

We propose an automated approach to modeling drainage channels—and, more generally, linear features that lie on the terrain—from multiple images. It produces models of the features and of the surrounding terrain that are accurate and consistent and requires only minimal human intervention.We take advantage of geometric constraints and photommetric knowledge. First, rivers flow downhill and lie at the bottom of valleys whose floors tend to be either V- or U-shaped. Second, the drainage pattern appears in gray-level images as a network of linear features that can be visually detected.Many approaches have explored individual facets of this problem. Ours unifies these elements in a common framework. We accurately model terrain and features as 3-dimensional objects from several information sources that may be in error and inconsistent with one another. This approach allows us to generate models that are faithful to sensor data, internally consistent and consistent with physical constraints. We have proposed generic models that have been applied to the specific task at hand. We show that the constraints can be expressed in a computationally effective way and, therefore, enforced while initializing the models and then fitting them to the data. Furthermore, these techniques are general enough to work on other features that are constrained by predictable forces.

[1]  Pascal Fua,et al.  Using crest lines to guide surface reconstruction from stereo , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[2]  Pascal Fua,et al.  Model-Based Optimization: Accurate and Consistent Site Modeling , 1996 .

[3]  H. Silfverhielm,et al.  Sweden , 1996, The Lancet.

[4]  Pierre Leymarie,et al.  Correction to “Drainage Networks From Grid Digital Elevation Models” by John Fairfield and Pierre Leymarie , 1991 .

[5]  William A. Barrett,et al.  Intelligent scissors for image composition , 1995, SIGGRAPH.

[6]  Pascal Fua,et al.  Model driven edge detection , 1990, Machine Vision and Applications.

[7]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[8]  L. Duane Pyle,et al.  A simplex algorithm—gradient projection method for nonlinear programming , 1971, ACM '71.

[9]  John F. O'Callaghan,et al.  The extraction of drainage networks from digital elevation data , 1984, Comput. Vis. Graph. Image Process..

[10]  Josiane Zerubia,et al.  New Prospects in Line Detection by Dynamic Programming , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  J. A. Salvato John wiley & sons. , 1994, Environmental science & technology.

[12]  Laurent D. Cohen,et al.  Global Minimum for Active Contour Models: A Minimal Path Approach , 1997, International Journal of Computer Vision.

[13]  Gabriel Taubin,et al.  Estimating the tensor of curvature of a surface from a polyhedral approximation , 1995, Proceedings of IEEE International Conference on Computer Vision.

[14]  B. Wrobel THE EVOLUTION OF DIGITALPHOTOGRAMMETRYFROM ANALYTICAL PHOTOGRAMMETRY , 2006 .

[15]  Eberhard Gülch Results of test on image matching of ISPRS WG III/4 , 1991 .

[16]  Martin A. Fischler,et al.  Linear delineation , 1987 .

[17]  Philip E. Gill,et al.  Practical optimization , 1981 .

[18]  Dimitris N. Metaxas,et al.  Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Martin A. Fischler,et al.  Detection of roads and linear structures in low-resolution aerial imagery using a multisource knowledge integration technique☆ , 1981 .

[20]  Andrea J. van Doorn,et al.  Two-plus-one-dimensional differential geometry , 1994, Pattern Recognition Letters.

[21]  GeorgeA. Silver Switzerland , 1989, The Lancet.

[22]  Ch. Brechbuhler,et al.  Parameterisation of closed surfaces for 3-D shape description , 1995 .

[23]  Jan J. Koenderink,et al.  Local features of smooth shapes: ridges and courses , 1993, Optics & Photonics.

[24]  Michael A. Saunders,et al.  LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares , 1982, TOMS.

[25]  Norman I. Badler,et al.  Hierarchical Shape Representation Using Locally Adaptive Finite Elements , 1994, ECCV.

[26]  Julia Miller,et al.  The Principles of Geology , 1905, Nature.

[27]  Guido Gerig,et al.  Parametrization of Closed Surfaces for 3-D Shape Description , 1995, Comput. Vis. Image Underst..

[28]  Dr. Marsha Jo Hannah,et al.  Digital Stereo Image Matching Techniques , .

[29]  Pascal Fua,et al.  Imposing Hard Constraints on Soft Snakes , 1996, ECCV.

[30]  L. Band Topographic Partition of Watersheds with Digital Elevation Models , 1986 .

[31]  Anthony Hoogs,et al.  RADIUS common development environment , 1992, Other Conferences.

[32]  Pascal Fua,et al.  Taking Advantage of Image-Based and Geometry-Based Constraints to Recover 3-D Surfaces , 1996, Comput. Vis. Image Underst..

[33]  Christian Heipke,et al.  Integration of Digital Image Matching and Multi Image Shape from Shading , 1992, DAGM-Symposium.

[34]  Steven W. Zucker,et al.  Inferring Surface Trace and Differential Structure from 3-D Images , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  R. Fletcher Practical Methods of Optimization , 1988 .

[36]  Pascal Fua,et al.  Object-centered surface reconstruction: Combining multi-image stereo and shading , 1995, International Journal of Computer Vision.

[37]  Demetri Terzopoulos,et al.  A finite element model for 3D shape reconstruction and nonrigid motion tracking , 1993, 1993 (4th) International Conference on Computer Vision.