An Improved Urban Mapping Strategy Based on Collaborative Processing of Optical and SAR Remotely Sensed Data

Potential of combining SAR and optical remotely sensed data for rapid urban mapping is highlight. Two groups of optical and SAR remotely sensed data are selected to evaluate the strategy. Outputs are verified and analyzed from 3 aspects. The single class and merged map accuracy are evaluated; the proposed method is compared with 2 mature algorithms; the selected classifiers are applied to 7 different fusion algorithms to make further comprehension. The outcomes illustrate the potential of synergic optical and SAR data for monitoring urbanization status and demonstrate that the proposed SAR/optical information synergy method improved the capabilities of urban mapping compared with separately using SAR and optical data. The results demonstrate that the proposed method can map built-up area, water body, and vegetation at accuracy of 99.31%, 91.92%, and 91.72%, respectively. These results are much better than when solo optical or SAR data was selected and better than classification results based on mature fusion methods. The main contributions of this article are as follows: the proposal of a rapid urban mapping strategy based on integration of optical and SAR data and the verifying and analysis of potential of synergic optical and SAR data for rapid urban mapping.