Automatic Framework of Mapping Impervious Surface Growth With Long-Term Landsat Imagery Based on Temporal Deep Learning Model

作者：Ranyu Yin, Guojin He, Guizhou Wang, Tengfei Long, Hongfeng Li, Dengji Zhou, Chengjuan Gong · 发表于：IEEE Geoscience and Remote Sensing Letters · 年份：2021 · DOI：10.1109/lgrs.2021.3135869 · 被引用次数：15 · 研究领域：Urban Heat Island Mitigation、Land Use and Ecosystem Services、Urban Green Space and Health

The impervious surface (IS) cover and its dynamics are key parameters in research about urban and ecology. This letter proposed an automatic framework to map the IS growth end-to-end based on the temporal deep learning (DL) model and long time-series Landsat imagery. First, the training and validating datasets were auto-generated by a joint strategy. Then, a DL network was designed, and the IS growth was predicted in temporal windows. Finally, the results from multi-temporal windows are combined to generate the IS growth map. The data around the core of Beijing, China, is tested, and the result shows that the proposed method could: 1) efficiently model the IS growth; 2) map IS growth with less salt-and-pepper noise and false alarm compared to existing products; and 3) be extended to future data easily.