Cross-city Landuse classification of remote sensing images via deep transfer learning

This paper describes a deep transfer model which consists of multiple sub-networks which are independently optimized by a supervised task-oriented loss and an unsupervised consistency loss. The former loss function utilizes annotations to accomplish the designed goal. The latter loss prompts the network to learn the target domain data distribution and encourages multiple sub-networks to share learned knowledge. We utilize the proposed model to work with a global local climate zone classification. For the dataset, the source domain includes 352,366 training samples from 42 cities, and the target domain has 48,307 samples from 10 other cities. According to our experiments, the proposed model improves 3.46% overall accuracy and 2.97% average accuracy when compared with other state-of-the-art domain adaptation methodologies. Besides, the classification maps also visualize the outstanding performance of the proposed deep transfer network.