Rooftop extraction method for high spatial resolution remote sensing image based on sparse representation

In existing rooftop extraction methods, either too few or too many features in high spatial resolution remote-sensing image (HSRRSI) are used, reducing the rooftop extraction accuracy. Accordingly, a rooftop extraction method for HSRRSI based on sparse representation (SR) is proposed in this work. The optimal segmentation parameters are first determined by the ratio of mean difference to neighbours to standard deviation index method and maximum area method. Thereafter, the optimal feature subset of HSRRSI is constructed on the basis of the L ( 1 ) regularization SR model to remove redundant features. Finally, a random forest classifier is used to extract rooftops based on the optimal feature subset. Results show that the overall accuracy of the two study areas in Zhanggong District are 0.91776 and 0.88313, respectively. This study can help in effectively extracting rooftops from HSRRSI, which is of great significance in urban planning, population statistics and economic forecasting.

Automated summary

This paper proposes a rooftop extraction method for high spatial resolution remote-sensing images based on sparse representation. The method improves extraction accuracy by determining optimal segmentation parameters and constructing an optimal feature subset. The overall accuracy of the proposed method in two study areas in Zhanggong District is 0.91776 and 0.88313, respectively. This study is of great significance in urban planning, population statistics, and economic forecasting.