Boosting for Domain Adaptation Extreme Learning Machines for Hyperspectral Image Classification

Domain adaptation and transfer learning adapt the priori information of source domain to train a classier used to predict the label in the target domain. The parameter and instance transfer methods have shown excellent performance. The former adjusts the parameters of transitional classifiers and the latter re-weights the training sample to the different training set, which is similar to the AdaBoost. To further improve the performance, we proposed to combine the two techniques mentioned above. More specifically, we select the Transfer Boosting and domain adaptation extreme learning machine (DAELM) as the instance and parameter transfer methods, respectively. We refer the proposed method to the boosting for DAELM (BDAELM). We compare the proposed method with DAELM and other methods on the real cross-domain hyperspectral remote sensing images acquired over a Japanese mixed forest, showing improved classification accuracies.