Abstract:

A new approach based on Gaussian error function back-propagation(Erf-BP)neural network was developed to analyze mixture pixels and was applied in forest area in Anji County, Zhejiang Province. The study results showed that Erf-BP model was superior to unconstrained linear spectral mixture analysis model and maximum likelihood method. Through collecting sample plots from the high-resolution satellite imagery to evaluate accuracy, the results showed: the total accuracy yielded 89.2% for the Erf-BP model, and RMSE was approximately 39% lower than unconstrained linear spectral mixture analysis model.For forest information extraction, the accuracy yielded 86% for the Erf-BP model, and RMSE was approximately 40.6% lower than unconstrained linear spectral mixture analysis model. At the same time, compared the area percent of each endmember estimated from the three methods with forest resource inventory data, the results showed the accuracy of Erf-BP model (RMSE=4.18%) was slightly higher than maximum likelihood method (RMSE=7.90%) and obviously higher than unconstrained linear spectral mixture analysis model (RMSE=18.75%). Erf-BP model was a feasible method to extract remote information of different forest types, even of different tree species.

Key words: Gaussian error function, back-propagation algorithm, spectral mixture analysis, forest