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

Abstract

Urban green space plays a crucial role in the construction of ecological city and livable environment. While multi-temporal 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 multi-temporal 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 multi-temporal 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. Experimental results show that multi-training outperforms self-training, co-training, tri-training, and super-training on both accuracy and consistency on multi-temporal 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.

Urban Trees Expansion and Exposure Improvement in China: Insights From Satellite Observations (1985–2023)

“…Future studies could benefit from incorporating higher-resolution imagery and groundtruthing data to improve the precision of tree cover assessments [56], [57], [58]. The study period (1985-2023) captures longterm trends in urban tree cover expansion; however, shorter time intervals may be necessary to capture more nuanced changes and their drivers [59]. Future research could focus on shorter time intervals to provide finer-grained insights into tree cover dynamics.…”