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Cross-domain scene classification identifies scene categories by learning knowledge from a labeled data set (source domain) to an unlabeled data set (target domain), where the source data and the target data are sampled from different distributions. A lot of domain adaptation methods are used to reduce the distribution shift across domains, and most existing methods assume that the source domain shares the same categories with the target domain. It is usually hard to find a source domain that covers all categories in the target domain. Some works exploit multiple incomplete source domains to cover the target domain. However, in such setting, the categories of each source domain are a subset of the target-domain categories, and the target domain contains “unknown” categories for each source domain. The existence of unknown categories results in the conventional domain adaptation unsuitable. Known and unknown categories should be treated separately. Therefore, a separation mechanism is proposed to separate the known and unknown categories in this article. First, multiple-source classifiers trained on the multiple source domains are used to coarsely separate the known/unknown categories in the target domain. The target images with high similarities to source images are selected as known categories, and the target images with low similarities are selected as unknown categories. Then, a binary classifier trained using the selected images is used to finely separate all target-domain images. Finally, only the known categories are implemented in the cross-domain alignment and classification. The target images get labels by integrating the hypotheses of multiple-source classifiers on the known categories. Experiments are conducted on three cross-domain data sets to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Cross-domain scene classification refers to the scene classification task in which the training set (termed source domain) and the test set (termed target domain) come from different distributions. Various domain adaptation methods have been developed to reduce the distribution discrepancy between different domains. However, current domain adaptation methods assume that the source domain and target domain share the same categories. In reality, it is hard to find a source domain that can completely cover all the categories of target domain. In this article, we propose to use multiple complementary source domains to form the categories of target domain. A multisource compensation network (MSCN) is proposed to tackle these challenges: distribution discrepancy and category incompleteness. First, a pretrained convolutional neural network (CNN) is exploited to learn the feature representation for each domain. Second, a cross-domain alignment module is developed to reduce the domain shift between source and target domains. Domain shift is reduced by mapping the two domain features into a common feature space. Finally, a classifier complement module is proposed to align categories in multiple sources and learn a target classifier. Two cross-domain classification data sets are constructed using four heterogeneous remote sensing scene classification data sets. Extensive experiments are conducted on these datasets to validate the effectiveness of the proposed method. The proposed method can achieve 81.23% and 81.97% average accuracies on two-source-complementary data set and three-source-complementary data set, respectively. [ABSTRACT FROM AUTHOR]