Extreme Learning Machine-Based Ensemble Transfer Learning for Hyperspectral Image Classification

Although the extreme learning machine (ELM) has been successfully applied to hyperspectral image (HSI) classification, the development of the ELM is restricted by insufficient training data. In this article, we propose a novel extreme learning machine-based ensemble transfer learning algorithm for hyperspectral image classification named TL-ELM. TL-ELM not only retains the input weights and hidden biases of the ELM learned from the target domain, but also utilizes instances in the source domain to iteratively adjust the output weights of the ELM, which are used as the weights of the training models, and then ensembles the training models with their weights for the final classification. In experiments, we choose different regions in northern Italy, namely, Pavia University and Pavia Centre, as the source dataset and target dataset, respectively, and through a comparison with other transfer learning algorithms, we demonstrate that our proposed TL-ELM algorithm is superior on HSI classification tasks with only a few labeled data points in the target domain. Furthermore, we set Pavia University as the source dataset and Pavia Centre as the target dataset to demonstrate that our proposed method can effectively transfer useful instances between different HSIs.