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Proceedings Paper

Hybrid method to improve abundance estimation of hyperspectral mixture pixel
Author(s): Bo Wu; Yindi Zhao
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Paper Abstract

A hybrid method integrated wavelet spectral feature with total least square algorithm for improving abundance estimation of hyper-spectral mixture pixels is proposed. The method uses the wavelet transform as a pre-processing step for the spectral feature extraction to decrease the within end-member variability, and then utilizes total least square (TLS) algorithm to capture the spectral variations between end-members. The hybrid method can take both technique advantages to reduce the impact of spectral variations with different format. Consequently, the approach provides a potential ability to reduce and tackle within end-member variation inherent in real mixture pixels, and hence to improve abundance estimation. Experiment of simulating mixture spectral data is conducted to validate the procedures, and the results demonstrate that the proposed method can reduce the abundance estimation deviation over 20% on average in the case of spectral end-member variations, as compared to that of the original hyper-spectral signals with least square estimation approach does. Comparisons with the decomposition of wavelet based features (DWT) and total least square have also been implemented, and the experiment shows the hybrid method can also improve the abundance estimation by 5%-10% than those of DWT and TLS do in terms of average RMSE.

Paper Details

Date Published: 15 November 2007
PDF: 7 pages
Proc. SPIE 6787, MIPPR 2007: Multispectral Image Processing, 67872H (15 November 2007); doi: 10.1117/12.747699
Show Author Affiliations
Bo Wu, Fuzhou Univ. (China)
The Chinese Univ. of Hong Kong (Hong Kong China)
Yindi Zhao, China Univ. of Mining and Technology (China)

Published in SPIE Proceedings Vol. 6787:
MIPPR 2007: Multispectral Image Processing
Henri Maître; Hong Sun; Jianguo Liu; Enmin Song, Editor(s)

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