 This paper proposes a computationally efficient deep learning architecture for satellite classification of complex environments using convolutional neural networks, CNNs, which outperforms other well-known CNNs and machine learning algorithms in terms of mean overall accuracy, significantly improving the accuracy of wetland classes. This article was authored by Ali Jamali, Massoud Madyampri, Brian Briscoe, and others.