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Rapid Urban Mapping Using SAR/Optical Imagery Synergy.

Christina Corbane1, Jean-François Faure2, Nicolas Baghdadi3

  • 1ESPACE Unit, Institut de Recherche pour le Développement, Maison de la télédétection, 500 rue JF Breton, F34093 Montpellier cedex 5, France. christina.corbane@ird.fr.

Sensors (Basel, Switzerland)
|November 23, 2016
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Summary

Combining Synthetic Aperture Radar (SAR) and optical data offers a powerful approach for rapid urban mapping. This unsupervised method enhances urban area extraction and monitoring, proving valuable for disaster management and urban planning.

Keywords:
SAR sensorsfuzzy K-means classificationinformation fusionoptical sensorsrapid urban mappingtexture analysis

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Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Urban Studies

Background:

  • Operational rapid urban mapping is crucial for timely decision-making.
  • Integrating diverse remote sensing data sources can improve mapping accuracy.
  • Existing methods may not fully leverage the complementary information from SAR and optical sensors.

Purpose of the Study:

  • To develop and validate an unsupervised algorithm for fusing Synthetic Aperture Radar (SAR) and optical data for urban mapping.
  • To assess the effectiveness of the combined SAR/optical approach compared to using individual sensors.
  • To demonstrate the utility of the method for monitoring urbanization and supporting disaster management.

Main Methods:

  • A two-stage unsupervised algorithm processing co-registered SAR/optical image pairs.
  • Texture analysis using eight chain-based Gaussian models applied independently to SAR and optical images.
  • Fuzzy K-means partitioning of texture images followed by a fuzzy decision rule for data fusion.

Main Results:

  • The developed SAR/optical information fusion scheme significantly improved urban area extraction capabilities.
  • The method demonstrated suitability for monitoring urbanization development across diverse study areas.
  • Validation on Bucharest and Cayenne confirmed the enhanced performance over separate sensor utilization.

Conclusions:

  • Combining SAR and optical data through the proposed unsupervised fusion scheme holds significant potential for operational rapid urban mapping.
  • The approach is effective for analyzing urban areas, supporting disaster management, and planning in urban sprawl.
  • The findings underscore the value of multi-sensor data fusion for timely urban environment analysis.