Change Detection in Overhead Imagery Using Neural Networks
Applied Intelligence
ICIT '06 Proceedings of the 9th International Conference on Information Technology
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 04
Error minimized extreme learning machine with growth of hidden nodes and incremental learning
IEEE Transactions on Neural Networks
Classification ability of single hidden layer feedforward neural networks
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
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Nowadays, land-cover change detection plays a more and more important role in environment protection and many other fields. However, the current land-cover change detection methods encounter the problems of low accuracy and low efficiency, especially in dealing with large scale remote sensing (RS) data. This paper presents a novel extreme learning machine (ELM) based land-cover change detection method with high testing accuracy and fast processing speed. The evaluation results show that ELM outperforms the traditional methods, e.g., SVM and BP network, in terms of training speed and generalization performance, when applied in land-cover classification. In our experiments, we apply our method to the analysis of rapid land use change in Taihu Lake region over the past decade.