Hybrid method to improve abundance estimation of hyperspectral mixture pixel

Bo Wu; Yindi Zhao

doi:10.1117/12.747699

15 November 2007 Hybrid method to improve abundance estimation of hyperspectral mixture pixel

Bo Wu, Yindi Zhao

Proceedings Volume 6787, MIPPR 2007: Multispectral Image Processing; 67872H (2007) https://doi.org/10.1117/12.747699
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

Citation Download Citation

Bo Wu and Yindi Zhao "Hybrid method to improve abundance estimation of hyperspectral mixture pixel", Proc. SPIE 6787, MIPPR 2007: Multispectral Image Processing, 67872H (15 November 2007); https://doi.org/10.1117/12.747699

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KEYWORDS

Wavelets

Discrete wavelet transforms

Signal to noise ratio

Wavelet transforms

Feature extraction

Error analysis

Roads

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