Unsupervised Transfer Learning Using for Multi-Model Remote Sensing Data Classification

Traditional supervised classification has been successfully applied to remote sensing (RS) classification and thematic mapping. However, labeling data is usually time-consuming and expensive. Transfer learning (TL) is able to reduce the labeling labor by utilizing pre-existing labeled data (source domain). Most of the existing TL methods applied in remote sensing still need a few labeled samples in target domain (where to be classified) or interaction between the classifier and users. By transferring the data distributions between the source and target domain into a joint feature space (where the distribution discrepancy between the source and target domains is smaller than the original space), a novel unsupervised transfer learning framework is proposed in this paper. In our framework, robust remote sensing indices are extracted from Digital Surface Models (DSM) and the associated orthophoto to help us to estimate the conditional probability distribution of the target domain. After that, our proposed Dispersion-optimized Joint Distribution Alignment (DJDA) method is able to generate transformed feature representations of source and target feature sets through simultaneous reduction of the joint distribution discrepancy between two domains and optimizing the dispersion (both intra-class dispersion and inter-class dispersion). In our experiments, the proposed framework is able to improve the transferability between multi-model datasets (Orthophoto + DSM). The comparative experiments demonstrate that the proposed DJDA outperforms the compared state-of-the-art unsupervised domain adaptation methods on RS image classification.