An Efficient Convolutional Neural Network for Remote-Sensing Scene Image Classification

Deep neural networks are providing a powerful solution for remote-sensing scene image classification. However, a limited number of training samples, inter-class similarity among scene categories, and to get the benefits of multi-layer features remains a significant challenge in the remote sensing domain. Many efforts have been proposed to deal the above challenges by adapting knowledge of state-of-the-art networks such as AlexNet, GoogleNet, OverFeat, etc. However, these networks have high number of parameters. This research proposes a five-layer architecture which has fewer parameters compared with above state-of-the-art networks, and can be also complementary to other convolutional neural network features. Extensive experiments on UC Merced and WHU-RS datasets prove that although our network decreases the number of parameters dramatically, it generates more accurate results than AlexNet, OverFeat, and its accuracy is comparable with other state-of-the-art methods.