A Novel Multi-Training Method for Time-Series Urban Green Cover Recognition From Multitemporal Remote Sensing Images

Urban green space plays a crucial role in the construction of ecological city and livable environment. While multitemporal remote sensing images provide strong support for urban green cover monitoring, they often suffer from
data shifting
, where the data distribution varies from phase to phase. Designing a general multitemporal framework to extract urban green cover is challenging, mainly due to possible time-consuming data labeling and inconsistent prediction. To address that, we propose multi-training, a novel method for land cover classification on multitemporal remote sensing images. Multi-Training is a two-stage method to independently train classifier on each phase in the training stage and then to combine the information from all the classifiers in the communication stage. As a semi-supervised learning method, multi-training adopts a new rule to obtain the confidence of unlabeled samples’ prediction, which reduces the dependence on labeled data and increases the result's consistency between phases. The experimental results show that multi-training outperforms self-training, co-training, tri-training, and super-training on both accuracy and consistency on multitemporal remote sensing image datasets. Furthermore, we have analyzed the necessary parameters in our method and conclude that the number and the combination of phases will dominate the prediction results.