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Detecting Unknown Artificial Urban Surface Materials Based on Spectral Dissimilarity Analysis.

Marianne Jilge1, Uta Heiden2, Martin Habermeyer3

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This study introduces a method to improve urban spectral libraries using spectral information divergence and spectral correlation angle (SID-SCA). It effectively identifies and categorizes unknown urban materials, enhancing spectral library completeness for urban applications.

Keywords:
dissimilarityimaging spectroscopyspectral libraryunknown surface materialsurban areas

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Area of Science:

  • Remote Sensing
  • Urban Planning
  • Geospatial Analysis

Background:

  • High-resolution imaging spectroscopy data are crucial for urban material inventories.
  • Existing urban spectral libraries face limitations in regional and sensor transferability due to diverse surface materials.
  • Incomplete spectral libraries hinder accurate urban material mapping.

Purpose of the Study:

  • To develop a methodology for utilizing incomplete urban spectral libraries.
  • To detect and categorize unknown urban surface materials.
  • To enhance the generation of image-specific training databases for urban applications.

Main Methods:

  • Developed a methodology assuming unknown spectra are dissimilar to known spectra in a basic spectral library (BSL).
  • Applied the Spectral Information Divergence-Spectral Correlation Angle (SID-SCA) similarity measure to detect unknown urban surfaces.
  • Categorized detected unknown materials using spectral and spatial metrics.

Main Results:

  • Successfully redetected erased material classes and identified new materials like solar panels.
  • Demonstrated that BSL incompleteness and dissimilarity thresholds influence detection and intra-class variability.
  • SID-SCA effectively detected unknown materials while minimizing spectral mixture issues.

Conclusions:

  • The dissimilarity analysis overcomes limitations of incomplete urban spectral libraries.
  • The methodology enables the creation of robust, image-specific training databases.
  • Improved spectral library completeness enhances urban material inventory accuracy for diverse applications.