Remote sensing image based bamboo forest monitoring with a back propagation(BP) neural network

To estimate the carbon content of bamboo forest based on remote sensing,highly accurate data acquisition is necessary to reduce estimation errors.In this study,enhanced thematic mapper plus(ETM+) remote sensing data was used to extract bamboo forest data using a back propagation(BP) neural network.Matlab program language(Version 7.1) was used to compile the classification algorithm with algorithms of three training functions being compared;namely,Traingda-gradient descent backpropagation with adaptive learning rate backpropagation;Trainlm-levenberg-marquardt backpropagation;and Traingdm-gradient descent with momentum backpropagation.Results showed that for bamboo forest the BP neural network had a high classification accuracy with a producer accuracy of 84.0% and a user accuracy of 98.7%.Meanwhile,of the three different training functions Traingda had the highest classification accuracy,whereas Trainlm had the shortest training time.