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Proceedings Paper

Identifying pure urban image spectra using a learning urban image spectral archive (LUISA)
Author(s): Marianne Jilge; Uta Heiden; Martin Habermeyer; André Mende; Carsten Juergens
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Paper Abstract

In this study a learning urban image spectral archive (LUISA) has been developed, that overcomes the issue of an incomplete spectral library and can be used to derive scene-specific pure material spectra. It consists of a well described starting spectral library (LUISA-A) and a tool to derive scene-based pure surface material spectra (LUISA-T). The concept is based on a three-stage approach: (1) Comparing hyperspectral image spectra with LUISA-A spectra to identify scene-specific pure materials, (2) extracting unknown pure spectra based on spatial and spectral metrics and (3) provides the framework to implement new surface material spectra into LUISA-A. The spectral comparison is based on several similarity measures, followed by an object- and spectral-based ruleset to optimize and categorize potentially new pure spectra.

The results show that the majority of pure surface materials could be identified using LUISA-A. Unknown spectra are composed of mixed pixels and real pure surface materials which could be distinguished by LUISA-T.

Paper Details

Date Published: 26 October 2016
PDF: 14 pages
Proc. SPIE 10008, Remote Sensing Technologies and Applications in Urban Environments, 100080J (26 October 2016); doi: 10.1117/12.2241370
Show Author Affiliations
Marianne Jilge, Ruhr-Univ. Bochum (Germany)
Uta Heiden, German Aerospace Ctr. (Germany)
Martin Habermeyer, German Aerospace Ctr. (Germany)
André Mende, Administrative District Office Zwickau (Germany)
Carsten Juergens, Ruhr-Univ. Bochum (Germany)

Published in SPIE Proceedings Vol. 10008:
Remote Sensing Technologies and Applications in Urban Environments
Thilo Erbertseder; Thomas Esch; Nektarios Chrysoulakis, Editor(s)

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