Segmentation of high-resolution remote sensing image combining phase congruency with local homogeneity

  • Chao WangEmail author
  • Aiye Shi
  • Xuehong Zhang
  • Qian Liu
Original Paper


Realizing both of the effective weak edge detection and fake edge suppression is an extremely challenging problem facing high-resolution remote sensing (HRRS) image segmentation. To address the problem, an HRRS image segmentation method combining phase congruency with local homogeneity is proposed by advantageous complementarities. Firstly, the Log Gabor Filter is used to extract phase congruency information. Then, the local homogeneity index J value is adopted to optimize the edge detection results. On this basis, an objective function optimization strategy based on minimizing inter-scale mutual information is proposed, and a parameter-adaptive model of edge response is established. In the end, the segmentation results are obtained by multi-scale region segmentation and merging based on this model. Two sets of HRRS images are used in experiments, and the results are compared with the J value-/phase congruency-based models, the well-known commercial software e-Cognition, and a traditional gradient-based segmentation method, respectively. Both visual and quantitative evaluations have demonstrated the effectiveness of the proposed method.


High resolution Remote sensing Image segmentation Phase congruency Local homogeneity 



Thanks for the effort of Prof. Xing Hongyan (Nanjing University of Information Science and Technology) and Prof. Liu Hui (Jiangxi University of Science and Technology) during the first revised round.

Funding information

This study is supported by the National Natural Science Foundation of China (Grant 61601229 and 41871239), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 16KJB510022), the Natural Science Foundation of Jiangsu Province (No. BK20160966), and the Jiangsu Overseas Scholar Program for University Prominent Young & Middle-aged Teachers and Presidents (No. 2018069) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (No. 1081080009001).


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Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjingChina
  2. 2.College of Computer and Information EngineeringHohai UniversityNanjingChina
  3. 3.College of Food, Agricultural, and Environmental ServicesOhio State UniversityWoosterUnited States

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