Ensemble of transfer component analysis for domain adaptation in hyperspectral remote sensing image classification

Abstract

In this work, we address the problem of unsupervised domain transfer learning via an ensemble strategy in the context of classification between multiple hyperspectral images. The objective of domain adaption is to assign the label to an image of interest (the target image) using the labeled samples in the source image. The proposed method is based on the rotation-based ensemble and transfer component analysis (TCA). In this method, the feature space in both source and target image is divided into several disjoint feature subsets. Then, the features induced by the TCA technique in the source domain are used as the input space to a random forest (RF) classifier. Finally, the results achieved by each step are fused by a majority vote. We compare the proposed method, ensemble of TCA (E-TCA), with a regular RF and an RF with the reduced features by the TCA. Experiments on the real hyperspectral image acquired over a Japanese mixed forest show remarkable cross-image classification performances.