A MultiKernel Domain Adaptation Method for Unsupervised Transfer Learning on Cross-Source and Cross-Region Remote Sensing Data Classification

Abstract

Labeling remote sensing data for classification is labor-intensive and time-consuming. Transfer learning (TL), under such context, is attracting increasing attention as it aims to harness information from data set of other regions where labels are readily available. The central topic of concern is to homogenize the large disparities of feature distribution of different data set through domain adaptation (DA). This article proposes a novel DA method for unsupervised TL, namely, multikernel jointly domain matching (MKJDM), which by definition considers multiple kernels as opposed to the currently popular single-kernel methods for measuring the distances between distributions. The single-kernel methods minimize the distances of feature distribution between the source domain (data set with training labels) and the target domain (data set to be classified) through, for example, maximum mean discrepancy (MMD) metric, formed under a kernel function mapping, while the multikernel version (MK-MMD) uses different kernel functions to encapsulate multiple aspects of distribution discrepancies, and is, therefore, more capable of distance minimization. Our MKJDM implementation also considers simultaneously aligning marginal and class conditional distributions and reweight for each instance, which further improves the performance. Two experiments performed on remote sensing images and multi-modal data sets (i.e., Orthophoto and Digital Surface Models), with regions of different countries with distinctly different land patterns serving as source and target domain data, show that the overall accuracies are improved by 37.28% and 46.62% after applications of our MKJDM method. An additional comparative experiment with five state-of-the-art DA methods also demonstrates that our method achieves the best performance.