Time-series processing of large scale remote sensing data with extreme learning machine

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.