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Semi-supervised Remote Sensing Image Segmentation Using Dynamic Region Merging

  • Ning He
  • Ke Lu
  • Yixue Wang
  • Yue Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8008)

Abstract

This paper introduces a remote sensing image segmentation approach by using semi-supervised and dynamic region merging. In remote sensing images, the spatial relationship among pixels has been shown to be sparsely represented by a linear combination of a few training samples from a structured dictionary. The sparse vector is recovered by solving a sparsity-constrained optimization problem, and it can directly determine the class label of the test sample. Through a graph-based technique, unlabeled samples are actively selected based on the entropy of the corresponding class label. With an initially segmented image based semi-supervised, in which the many regions to be merged for a meaningful segmentation. By taking the region merging as a labeling problem, image segmentation is performed by iteratively merging the regions according to a statistical test. Experiments on two datasets are used to evaluate the performance of the proposed method. Comparisons with the state-of-the-art methods demonstrate that the proposed method can effectively investigate the spatial relationship among pixels and achieve better remote sensing image segmentation results.

Keywords

Semi-supervised Remote Sensing Image Image segmentation Dynamic region merging 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ning He
    • 1
  • Ke Lu
    • 2
  • Yixue Wang
    • 3
  • Yue Gao
    • 4
  1. 1.Beijing Key Laboratory of Information Service EngineeringBeijing Union UniversityBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Shenyang Institute of EngineeringShenyangChina
  4. 4.School of ComputingNational University of SingaporeSingapore

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