Automatic Water Surface Sample Construction Method Based On Deep Learning and Its Application in Impervious Water Surface Recognition

Accurate and efficient Impervious surface mapping is crucial to natural ecological protection, urban planning, land use and other fields. Currently, using deep learning methods to extract Impervious surfaces requires a large number of training samples, and visual interpretation of the samples is time-consuming and laborious. The distribution of existing labeled samples is relatively limited, which will lead to model overfitting. This paper fully considers factors such as geographical differences and efficiency, and proposes an automatic extraction technology for Impervious surfaces combined with ecological geographical zoning. Two high-precision Impervious surface products, GISA and GLC_FCS30, are fused and superimposed, and training samples are obtained through the crowd-source data OpenStreetMap mapping optimization results. A deep learning framework based on ResNeSt introduced into ASPP is used to automatically construct Impervious surface samples in different ecological geographical divisions across the country. After testing with Gaofen-1 data in the internal test areas of each partition, the overall accuracy of impermeable surface extraction exceeded 90%. The sample migration characteristic experiment shows that the model extraction accuracy within the same ecological geographical zone is the highest and the effect is significant, and the overall accuracy of models in different ecological geographical zones is increased by at least 1.44%. The method of automatically constructing Impervious surface samples proposed in this article not only achieves ideal results in terms of accuracy, but also has geographical generalization.