Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 511-516, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-511-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 511-516, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-511-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

MAN-MADE OBJECT EXTRACTION FROM REMOTE SENSING IMAGERY BY GRAPH-BASED MANIFOLD RANKING

Y. He1, X. Wang1, X. Y. Hu2, and S. H. Liu1 Y. He et al.
  • 1Satellite Surveying and Mapping Application Center, National Administration of Surveying, Mapping and Geo-information, Beijing, P.R. China
  • 2Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, P.R. China

Keywords: Man-made Object Extraction, Remote Sensing Image, Priori Area Extraction, Graph Model, Manifold Ranking

Abstract. The automatic extraction of man-made objects from remote sensing imagery is useful in many applications. This paper proposes an algorithm for extracting man-made objects automatically by integrating a graph model with the manifold ranking algorithm. Initially, we estimate a priori value of the man-made objects with the use of symmetric and contrast features. The graph model is established to represent the spatial relationships among pre-segmented superpixels, which are used as the graph nodes. Multiple characteristics, namely colour, texture and main direction, are used to compute the weights of the adjacent nodes. Manifold ranking effectively explores the relationships among all the nodes in the feature space as well as initial query assignment; thus, it is applied to generate a ranking map, which indicates the scores of the man-made objects. The man-made objects are then segmented on the basis of the ranking map. Two typical segmentation algorithms are compared with the proposed algorithm. Experimental results show that the proposed algorithm can extract man-made objects with high recognition rate and low omission rate.