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Adaptive Linear Spectral Mixture Analysis

Chang, Chein-I

This paper presents a theory of adaptive linear spectral mixture analysis (ALSMA), which can implement LSMA using an adaptive linear mixing model (ALMM) that adjusts and varies with spectral signatures adaptively. In doing so, a recursive LSMA (RLSMA) is developed for ALSMA to allow LSMA to update spectral signature by spectral signature without reprocessing LSMA and also to fuse LSMA results obtained by ALMM using different sets of spectral signatures. To form ALMM, the concept of RLSMA-specified virtual dimensionality is further proposed for ALSMA, which not only can find spectral signatures recursively by RLSMA to adjust ALMM but also can automatically determine the number of spectral signatures via Neyman-Pearson detection theory.

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Adaptive Linear Spectral Mixture Analysis

Chang, Chein-I

This paper presents a theory of adaptive linear spectral mixture analysis (ALSMA), which can implement LSMA using an adaptive linear mixing model (ALMM) that adjusts and varies with spectral signatures adaptively. In doing so, a recursive LSMA (RLSMA) is developed for ALSMA to allow LSMA to update spectral signature by spectral signature without reprocessing LSMA and also to fuse LSMA results obtained by ALMM using different sets of spectral signatures. To form ALMM, the concept of RLSMA-specified virtual dimensionality is further proposed for ALSMA, which not only can find spectral signatures recursively by RLSMA to adjust ALMM but also can automatically determine the number of spectral signatures via Neyman-Pearson detection theory.

Adaptive Linear Spectral Mixture Analysis

Chang, Chein-I

This paper presents a theory of adaptive linear spectral mixture analysis (ALSMA), which can implement LSMA using an adaptive linear mixing model (ALMM) that adjusts and varies with spectral signatures adaptively. In doing so, a recursive LSMA (RLSMA) is developed for ALSMA to allow LSMA to update spectral signature by spectral signature without reprocessing LSMA and also to fuse LSMA results obtained by ALMM using different sets of spectral signatures. To form ALMM, the concept of RLSMA-specified virtual dimensionality is further proposed for ALSMA, which not only can find spectral signatures recursively by RLSMA to adjust ALMM but also can automatically determine the number of spectral signatures via Neyman-Pearson detection theory.

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Title:

Adaptive Linear Spectral Mixture Analysis

Contributors:

Chang, Chein-I (author)

Published in:

IEEE transactions on geoscience and remote sensing ; 55

Publication date:

2017

ISSN:

DOI:

https://doi.org/10.1109/TGRS.2016.2620494

Type of media:

Article (Journal)

Type of material:

Print

Language:

English

Keywords:

Fernerkundung , Messtechnik , Zeitschrift , Elektronik , Geowissenschaften

Classification:

Local classification TIB: 770/3710/5670
BKL: 38.03 Methoden und Techniken der Geowissenschaften / 74.41 Luftaufnahmen, Photogrammetrie

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