Studying the Effect of Activation Function on Classification Accuracy Using Deep Artificial Neural Networks

Abstract

Artificial Neural Networks (ANN) is widely used in remote sensing classification. Optimizing ANN still an enigmatic field of research especially in remote sensing. This research work is a trial to discover the ANN activation function to be used perfectly in classification (landcover mapping). The first step is preparing the reference map then assume a selected activation function and receive the ANN fuzzified output. The last step is comparing the output with the reference to reach the accuracy assessment. The research result is fixing the activation function that is perfect to be used in remote sensing classification. A real multi-spectral Landsat 7 satellite images were used and was classified (using ANN) and the accuracy of the classification was assessed with different activation functions. The sigmoid function was found to be the best activation function.