IEEE Trans Image Process - Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model.

Tópicos

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Resumo

This paper studies a nonlinear mixing model for hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian noise. These nonlinear functions are approximated by polynomials leading to a polynomial post-nonlinear mixing model. We have shown in a previous paper that the parameters involved in the resulting model can be estimated using least squares methods. A generalized likelihood ratio test based on the estimator of the nonlinearity parameter is proposed to decide whether a pixel of the image results from the commonly used linear mixing model or from a more general nonlinear mixing model. To compute the test statistic associated with the nonlinearity detection, we propose to approximate the variance of the estimated nonlinearity parameter by its constrained Cram?r-Rao bound. The performance of the detection strategy is evaluated via simulations conducted on synthetic and real data. More precisely, synthetic data have been generated according to the standard linear mixing model and three nonlinear models from the literature. The real data investigated in this study are extracted from the Cuprite image, which shows that some minerals seem to be nonlinearly mixed in this image. Finally, it is interesting to note that the estimated abundance maps obtained with the post-nonlinear mixing model are in good agreement with results obtained in previous studies.

Resumo Limpo

paper studi nonlinear mix model hyperspectr imag unmix nonlinear detect propos model assum pixel reflect nonlinear function pure spectral compon contamin addit white gaussian nois nonlinear function approxim polynomi lead polynomi postnonlinear mix model shown previous paper paramet involv result model can estim use least squar method general likelihood ratio test base estim nonlinear paramet propos decid whether pixel imag result common use linear mix model general nonlinear mix model comput test statist associ nonlinear detect propos approxim varianc estim nonlinear paramet constrain cramrrao bound perform detect strategi evalu via simul conduct synthet real data precis synthet data generat accord standard linear mix model three nonlinear model literatur real data investig studi extract cuprit imag show miner seem nonlinear mix imag final interest note estim abund map obtain postnonlinear mix model good agreement result obtain previous studi

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