ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2015, Vol. 52 ›› Issue (6): 1409-1423.doi: 10.7544/issn1000-1239.2015.20131422

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A Terrain Skeleton Feature Extraction Method Based on Morphological Encoding

Zhang Huijie, Liu Yaxin, Ma Zhiqiang, He Xinting, Bao Ning   

  1. (School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117) (Key Laboratory of Intelligent Information Processing of Jilin Universities (Northeast Normal University), Changchun 130117)
  • Online:2015-06-01

Abstract: Since the current profile recognition methods are not able to extract the precise skeleton and the special terrain feature, a new profile recognition method combined with morphology is proposed to solve these problems. In this method, the candidate points are extracted by the profile recognition, and then are connected into polygon stripe according to direction coefficients. Furthermore, the fulfill algorithms building the scale feature areas are put forward based on the polygon stripe by taking advantage of morphological strategy. In addition, multiple morphological codes are proposed to simplify the scale feature areas according to the concept of morphological erosion algorithm, and to obtain the scale feature lines. In order to satisfy the requirements about vector skeleton features in the various fields, the algorithms including restoration, detection and optimization are proposed to implement the transformation from the scale model to the vector model. Finally, a series of strategies about keeping the out-branches and recognizing the ring process are presented in this paper, which solve the problems about missing the long trunk lines and ring features in the result feature lines. These methods have been tested on the benchmark data and the real elevation data. As a result, the skeleton feature lines produced by our method outweigh the traditional method as a whole.

Key words: feature candidate point, scale feature area, morphology, pruning, target point recognition

CLC Number: