Joint Distance Transfer Metric Learning for Remote-Sensing Image Classification

Abstract

Most transfer learning methods have the problem of insufficient distance constraint that plays a very important role in improving image classification performance. Therefore, this letter proposes a new method called joint distance transfer metric learning (JDTML) for remote-sensing image classification. First, the JDTML method establishes the constraints of marginal distribution, intraclass distance, interclass distance, and intraclass divergence based on the maximum mean discrepancy. Second, the objective function is to combine these constraints. So, JDTML can not only reduce the differences between the two domains on the whole and in each class, but also gather the samples of the same class and expand the distance from each class to the rest classes. By solving the objective function, the transfer metric matrix is obtained. Finally, the source and target domains are transferred to a common subspace for dimension reduction. The data after dimension reduction is used for classification, and the accuracy of classification is improved by iteration. The experimental results show that JDTML is more accurate than other methods compared.