Combining Multiple Classifiers for Domain Adaptation of Remote Sensing Image Classification

Abstract

This article investigates the effectiveness of multiclassifier fusion technique on domain adaptation for remote sensing image classification. Since it is impossible to find a domain adaptation method that is optimal for different datasets, and it is also difficult to select the best base classifier for domain-invariant features, multiple domain adaptation fusion (MDAF) method and the multiple base classifier fusion (MBCF) method are proposed to achieve a more stable and superior classification performance. The most crucial step of the weighted fusion approach is to assign weights for classifiers. It is known that different classifiers have varied performances on different subsets of data, and therefore a samplewise adaptive weight is more desirable than a fixed one. For each sample, a desired weight should be able to characterize the reliability of a classifier, so that the advantages of different classifiers can be exploited. We propose a neighborhood consistency based adaptive weighting method, which assigns a large weight to a classifier on a sample if the prediction of the sample is consistent to the predictions of its local neighbors. Experiments with three remote sensing images demonstrate the efficiency of the proposed weighting strategy in the proposed MDAF and MBCF methods.