A comparison study of impervious surfaces estimation using optical and SAR remote sensing images

The estimation of impervious surface area (ISA) is becoming increasingly important because of its environmental and socio-economic significance. However, accurate ISA estimation remains challenging due to the diversity of impervious materials, as well as the occurrence of clouds in subtropical humid areas. In order to address these challenges and provide an accurate estimation of ISA in cloudy areas, it is advantageous to use both optical and microwave remote sensing which can penetrate cloud coverage. Our study aims to conduct a comprehensive comparison between these two data sources and between different methods for mapping ISA. Both the classification results and accuracy assessment provide a better understanding about the differences between Landsat ETM+ and ENVISAT ASAR images and between artificial neural network (ANN) and support vector machine (SVM) classifier for estimating the impervious surfaces. The comparison demonstrates that ETM+ images alone provide a better ISA estimation (OA: about 90%; Kappa: about 0.88) than the estimation from ASAR images alone (OA: about 85%; Kappa: about 0.77). Additionally, the experiment indicates that SVM should be a better choice for ISA estimation using Landsat ETM+ images, while ANN turns out to be more sensitive to the confusion between dry soils and bright impervious surfaces, and between shade and dark impervious surfaces. For ENVISAR ASAR images, ANN gets a better result with higher accuracy, while the SVM classifier produces more noise and has some edge effects.