Towards an Automatic Urban Settlement Mapping from Multi-Tomporal InSAR Trained by Social Media

Zelang Miao, Lixin Wu, Wenzhong Shi, Paolo Gamba, Mi Jiang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


A comprehensive understanding of the spatial distribution of urban settlements is significant to a series of research topics related to environmental and biological changes caused by the urbanization process. Among various technologies, Synthetic Aperture Radar (SAR) has been successfully applied in urban settlement mapping in the past two decades. Although much effort with varying degrees of success has been made in previous studies, the research work is still ongoing, and three challenges should be highlighted. First, the effect of de-speckling is usually underestimated, to the extent that the improvement of the SAR image quality is totally ignored in some studies. Second, a method that combines full Interferometric SAR (InSAR) information is as yet missing. Third, training samples are generally required to process SAR images to extract urban settlements, which is time-consuming and labor-intensive, or even impractical when classifying satellite data at the regional/global scale. To address these issues, this paper presents an automatic method for urban settlement mapping trained by multi-temporal InSAR using social media. To improve the detection performance and reduce false alarm ratio, intensity and coherence are first accurately estimated without loss of image resolution by homogeneous pixel selection and robust estimators. The homogeneous pixels will be also applied to measure urban characteristics from the geometrical prospective. After that, training samples are automatically generated from social media based on the fact that cities and urban areas are nowadays full of individual geo-referenced data such as social network data Finally, these multiple information sources will be fused to extract urban areas based on an improved one class classifier. Experimental results show that the proposed method is effective in extracting urban areas with good accuracy. This study provides a new de-speckling means to process multi-temporal InSAR and sheds new light on the applications of social media in the field of remote sensing.

Original languageEnglish
Title of host publication2018 Progress In Electromagnetics Research Symposium, PIERS-Toyama 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9784885523151
Publication statusPublished - 31 Dec 2018
Event2018 Progress In Electromagnetics Research Symposium, PIERS-Toyama 2018 - Toyama, Japan
Duration: 1 Aug 20184 Aug 2018

Publication series

NameProgress in Electromagnetics Research Symposium
ISSN (Print)1559-9450
ISSN (Electronic)1931-7360


Conference2018 Progress In Electromagnetics Research Symposium, PIERS-Toyama 2018

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials


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