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

ABSTRACT

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 L₁ 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.

KEYWORDS:

• Sparse representation
• High spatial resolution remote sensing images
• Rooftops
• Feature selection

Acknowledgements

This study is supported by the Youth Jinggang Scholars Program in Jiangxi Province (No. QNJG2020046) and the Program of Qingjiang Excellent Young Talents, Jiangxi University of Science and Technology (No. JXUSTQJBJ2018002). The authors would also like to acknowledge the contributions of Jacqueline Wah to the spelling and grammar check for this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the Youth Jinggang Scholars Program in Jiangxi Province [QNJG2020046]; Program of Qingjiang Excellent Young Talents, Jiangxi University of Science and Technology [JXUSTQJBJ2018002].