Performance Comparison of Machine Learning Methods Based on CNN for Satellite Imagery Classification

The processing of hyperspectral remote sensing images is a crucial field of study. As one of the most important phases in image processing right now, the classification task has attracted our attention. The Convolutional Neuronal Network (CNN) is one of the most demonstrative algorithms for deep learning since it is a sort of feed-forward neural network that uses convolutional computation. In this paper we proposed a customized model based on CNN applied on SAT 4 and SAT6 datasets. The experimental results outperformances methodology with accuracy value of 99.4%, loss of 2% and 99.8%, loss of 3.2% for SAT4 and SAT6, respectively.