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Spectral Angle Mapper (SAM) for anisotropy class indexing in imaging spectrometry data


Weyermann, J; Schläpfer, D; Hueni, A; Kneubühler, M; Schaepman, M E (2009). Spectral Angle Mapper (SAM) for anisotropy class indexing in imaging spectrometry data. In: SPIE Optics and Photonics 2009, San Diego, US, 2 August 2009 - 6 August 2009, 74570B.

Abstract

An empirical (target-) BRDF normalization method has been implemented for Imaging Spectrometry data processing,
following the approach of Kennedy, published in 1997. It is a simple, empirical method with the purpose of a rapid
technique, based on a least-squares quadratic curve fitting process. The algorithm is calculating correction factors in
either multiplicative or additive manner for each of the identified land cover classes, per spectral band and view angle unit. Image pre-classification is essential for successful anisotropy normalization. This anisotropy normalization method is a candidate to be used as baseline correction for future data products of APEX, a new airborne Imaging Spectrometer suitable for simulation and inter-calibration of data from various other sensors.
A classification algorithm, being able to provide anisotropy class indexing that is optimized for the purpose of BRDF normalization has to be used. In this study, the performance of the standard Spectral Angle Mapper (SAM) approach with RSL’s spectral database SPECCHIO attached is investigated. Due to its robustness regarding directional effects, SAM classification is estimated to be the most efficient. Results of both the classification and the normalization process are validated using two airborne image datasets from the HyMAP sensor, taken in 2004 over the "Vordemwald" test site in northern Switzerland.

Abstract

An empirical (target-) BRDF normalization method has been implemented for Imaging Spectrometry data processing,
following the approach of Kennedy, published in 1997. It is a simple, empirical method with the purpose of a rapid
technique, based on a least-squares quadratic curve fitting process. The algorithm is calculating correction factors in
either multiplicative or additive manner for each of the identified land cover classes, per spectral band and view angle unit. Image pre-classification is essential for successful anisotropy normalization. This anisotropy normalization method is a candidate to be used as baseline correction for future data products of APEX, a new airborne Imaging Spectrometer suitable for simulation and inter-calibration of data from various other sensors.
A classification algorithm, being able to provide anisotropy class indexing that is optimized for the purpose of BRDF normalization has to be used. In this study, the performance of the standard Spectral Angle Mapper (SAM) approach with RSL’s spectral database SPECCHIO attached is investigated. Due to its robustness regarding directional effects, SAM classification is estimated to be the most efficient. Results of both the classification and the normalization process are validated using two airborne image datasets from the HyMAP sensor, taken in 2004 over the "Vordemwald" test site in northern Switzerland.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:6 August 2009
Deposited On:05 Oct 2009 12:14
Last Modified:06 Dec 2017 20:32
Additional Information:Konferenzname auch: "Imaging Spectrometry XIV" ; in Proc. SPIE, Vol. 7457
Publisher DOI:https://doi.org/10.1117/12.825991

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