A New Skeleton Feature Extraction Method for Terrain Model Using Profile Recognition and Morphological Simplification

It is always difficul to reserve rings and main truck lines in the real engineering of feature extraction for terrain model. In this paper, a new skeleton feature extraction method is proposed to solve these problems, which put forward a simplification algorithm based on morphological theory to eliminate the noise points of the target points produced by classical profile recognition. As well all know, noise point is the key factor to influence the accuracy and efficiency of feature extraction. Our method connected the optimized feature points subset after morphological simplification; therefore, the efficiency of ring process and pruning has been improved markedly, and the accuracy has been enhanced without the negative effect of noisy points. An outbranching concept is defined, and the related algorithms are proposed to extract sufficient long trucks, which is capable of being consistent with real terrain skeleton. All of algorithms are conducted on many real experimental data, including GTOPO30 and benchmark data provided by PPA to verify the performance and accuracy of our method. The results showed that our method precedes PPA as a whole.

[1]  Guohe Huang,et al.  Building channel networks for flat regions in digital elevation models , 2009 .

[2]  Wolfgang Straßer,et al.  Terrain sketching , 2009, I3D '09.

[3]  Richard R. Jones,et al.  Algorithms for using a DEM for mapping catchment areas of stream sediment samples , 2002 .

[4]  Samir Akkouche,et al.  Feature based terrain generation using diffusion equation , 2010, Comput. Graph. Forum.

[5]  James E. Gain,et al.  Enhanced Texture‐Based Terrain Synthesis on Graphics Hardware , 2012, Comput. Graph. Forum.

[6]  Hans Hagen,et al.  Interactive Retro-Deformation of Terrain for Reconstructing 3D Fault Displacements , 2012, IEEE Transactions on Visualization and Computer Graphics.

[7]  J. Fairfield,et al.  Drainage networks from grid digital elevation models , 1991 .

[8]  M. Costa-Cabral,et al.  Digital Elevation Model Networks (DEMON): A model of flow over hillslopes for computation of contributing and dispersal areas , 1994 .

[9]  Yet-Chung Chang,et al.  Automatic extraction of ridge and valley axes using the profile recognition and polygon-breaking algorithm , 1998 .

[10]  Ian C. Fuller,et al.  Slope–channel coupling in steepland terrain: A field-based conceptual model from the Tarndale gully and fan, Waipaoa catchment, New Zealand , 2011 .

[11]  Xiangtao Li,et al.  An opposition-based differential evolution algorithm for permutation flow shop scheduling based on diversity measure , 2013, Adv. Eng. Softw..

[12]  Bedrich Benes,et al.  Interactive terrain modeling using hydraulic erosion , 2008, SCA '08.

[13]  Monia Santini,et al.  Hydrogeomorphic properties of simulated drainage patterns using digital elevation models: the flat area issue / Propriétés hydro-géomorphologiques de réseaux de drainage simulés à partir de modèles numériques de terrain: la question des zones planes , 2008 .

[14]  Fengxia Li,et al.  Notice of RetractionA 3D non-regular military symbol Deformation method along the Battlefield terrain , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[15]  Anna Rampini,et al.  Melted snow volume control in the snowmelt runoff model using a snow water equivalent statistically based model , 2012 .

[16]  M. Voltz,et al.  Effects of the spatial organization of agricultural management on the hydrological behaviour of a farmed catchment during flood events , 2001 .

[17]  Lorenzo Marchi,et al.  Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments , 2013 .

[18]  David H. Douglas,et al.  Detection of Surface-Specific Points by Local Parallel Processing of Discrete Terrain Elevation Data , 1975 .

[19]  Xiangtao Li,et al.  A perturb biogeography based optimization with mutation for global numerical optimization , 2011, Appl. Math. Comput..

[20]  A. Karnieli,et al.  Skeletonizing a DEM into a drainage network , 1995 .

[21]  Hervé Squividant,et al.  Incorporating landscape features to obtain an object‐oriented landscape drainage network representing the connectivity of surface flow pathways over rural catchments , 2011 .

[22]  Jordi J. Mallorquí,et al.  Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images , 2003, IEEE Trans. Geosci. Remote. Sens..

[23]  Richard Barnes,et al.  An efficient assignment of drainage direction over flat surfaces in raster digital elevation models , 2015, Comput. Geosci..

[24]  D. Tarboton.  A new method for the determination of flow directions and upslope areas in grid digital elevation models , 1997 .

[25]  James M. Rehg,et al.  Terrain Synthesis from Digital Elevation Models , 2007, IEEE Transactions on Visualization and Computer Graphics.

[26]  Andrew E. Johnson,et al.  Planetary-Scale Terrain Composition , 2009, IEEE Transactions on Visualization and Computer Graphics.

[27]  P. Tarolli,et al.  Drainage network detection and assessment of network storage capacity in agrarian landscape , 2013 .

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

[29]  T. I. Eldho,et al.  An enhanced technique in construction of the discrete drainage network from low-resolution spatial database , 2007, Comput. Geosci..

[30]  Joseph. Wood,et al.  The geomorphological characterisation of Digital Elevation Models , 1996 .

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

[32]  Patrick Marais,et al.  A system for real-time deformable terrain , 2011, SAICSIT '11.

[33]  Minghao Yin,et al.  Application of Differential Evolution Algorithm on Self-Potential Data , 2012, PloS one.