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Terrain analysis from curvature profiles
Author(s) -
Goldgof Dmitry B.,
Huang Thomas S.,
Lee Hua
Publication year - 1990
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.1850020303
Subject(s) - terrain , curvature , gaussian noise , gaussian curvature , invariant (physics) , gaussian , rotation (mathematics) , noise (video) , mathematics , algorithm , artificial intelligence , computer science , geometry , physics , image (mathematics) , quantum mechanics , mathematical physics , ecology , biology
This paper describes a new algorithm which uses Gaussian and mean curvature values of the terrain surface to extract feature points. These points are then used for recognition of particular subregions of the terrain and in estimating relative positions of these subregions in the terrain. The Gaussian and mean curvatures are chosen because they are invariant under rotation and translation. In the Gaussian and mean curvature images, the points of maximum and minimum curvatures are extracted and used for matching. The stability of the position of these points in the presence of noise and with resampling is investigated. The input for this algorithm is 3D digital terrain data. Curvature values are calculated from the data by fitting a quadratic surface over a square window and calculating directional derivatives of this surface. A method of surface fitting that is invariant to sensor‐centered coordinate system transformation is suggested and implemented. Real terrain data are used in our experiments. The algorithm is tested with and without the presence of noise, and its performance is described.