Mapping Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level
The proliferation of impervious surfaces results in a series of environmental issues, such as the decrease of vegetated areas and the aggravation of the urban heat island effects. The mapping of impervious surface and its spatial distributions is of significance for the ecological study of urban environment. Currently, the integration of optical and synthetic aperture radar (SAR) data has shown advantages in accurately characterizing impervious surface. However, the fusion mainly occurs at the
... ixel and feature levels which are subject to influences of data noises and feature selections, respectively. In this paper, an innovative and effective method was developed to extract urban impervious surface by synergistically utilizing optical and SAR images at the decision level. The objective of this paper was to obtain an accurate urban impervious surface map based on the random forest classifier and the evidence theory and to provide a detailed uncertainty analysis accompanying the fused impervious surface maps. In this study, both the GaoFen (GF-1) and Sentinel-1A imagery were first used as independent data sources for mapping urban impervious surfaces. Then additional spectral features and texture features were extracted and integrated with the original GF-1 and Sentinel-1A images in generating impervious surfaces. Finally, based on the Dempster-Shafer (D-S) theory, impervious surfaces were produced by fusing the previously estimated impervious surfaces from different datasets at the decision level. Results showed that impervious surfaces estimated from the combined use of original images and features yielded a higher accuracy than those from the original optical or SAR data. Further validations suggested that optical data was better than SAR data in separating impervious surfaces from non-impervious surfaces. The fused impervious surfaces at the decision level had a higher overall accuracy than those produced independently by optical or SAR data. It was also highlighted that the fusion of GF-1 and Sentinel-1A images reduced the amount of confusions among the low reflectance of impervious surface and water, as well as for low reflectance of bare land. An overall accuracy of 95.33% was achieved for extracting urban impervious surfaces by fused datasets. The spatial distributions of uncertainties provided by the evidence theory displayed a confidence level of at least 75% for the impervious surfaces derived from the fused datasets. Remote Sens. 2016, 8, 945 2 of 21 Remote Sens. 2016, 8, 945 3 of 21 impervious surface map by integrating the GF-1 and Sentinel-1A data at the decision-level based on the D-S theory and to provide detailed analyses of the uncertainty levels for the estimated impervious surfaces. Land cover types were first classified individually from the GF-1 (GaoFen-1 satellite) and Sentinel-1A imagery using the random forest (RF) technique and then fused together based on the D-S combination rules. Then the land cover types were further categorized as non-impervious surface (NIS) and impervious surface (IS). The accuracy assessment was performed by comparing estimated impervious surfaces against the reference data collected from the Google Earth imagery. Data and the Study Area The Study Area The study area covers the metropolitan region of Wuhan in the eastern Jianghan Plain (Figure 1 ). It has a tropical monsoon (humid) climate with abundant rainfall and four distinctive seasons. The annual mean temperature is 15.8 • C∼17.5 • C and the annual mean precipitation is approximately 1150 mm∼1450 mm. In the summer, the maximum air temperature in Wuhan can reach as high as 42 • C due to the terrain conditions (low and flat in the middle and hilly in the south) with the Yangtze and Han Rivers winding through the city. The main land cover types in the study area are vegetation, water bodies, bare lands, roads, residential areas and crops.