Identifying pure urban image spectra using a learning urban image spectral archive (LUISA)

Marianne Jilge; Uta Heiden; Martin Habermeyer; André Mende; Carsten Juergens

doi:10.1117/12.2241370

26 October 2016 Identifying pure urban image spectra using a learning urban image spectral archive (LUISA)

Marianne Jilge, Uta Heiden, Martin Habermeyer, André Mende, Carsten Juergens

Author Affiliations +

Proceedings Volume 10008, Remote Sensing Technologies and Applications in Urban Environments; 100080J (2016) https://doi.org/10.1117/12.2241370
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

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.

Citation Download Citation

Marianne Jilge, Uta Heiden, Martin Habermeyer, André Mende, and Carsten Juergens "Identifying pure urban image spectra using a learning urban image spectral archive (LUISA)", Proc. SPIE 10008, Remote Sensing Technologies and Applications in Urban Environments, 100080J (26 October 2016); https://doi.org/10.1117/12.2241370

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