Comparison of the extraction accuracy of urban impervious surfaces information based on remote sensing
Impervious surface is an important indicator to reflect the level of urban engineering construc-tion and it can be used to evaluate the quality of urban ecological environment.Therefore,efficient extraction of impervious surface is of great significance for urban expansion monitoring and ecosystem construction.D-InSAR technology is used in this paper to perform differential interferometry on Sentinel-1A images,generate coherence coefficient map,and combine bands with Landsat 8 images by taking the urban area of Shanghai City as the study area.Then a variety of machine learning algorithms are used to extract impervious surface information and the accuracy is compared.The results show that compared with a single Landsat 8 image,the maximum likelihood algorithm,SVM algorithm,CART algorithm and RF algorithm can be used to greatly improve the extraction accuracy of impervious surface information,and among which the RF algorithm can get the best accuracy,its overall accuracy can be 89.33％if the coherence coefficient map is added.
NETL
NSTL
万方数据
维普
