Author(s)

Yijia Liu¹, Jianhua Liu^1,2*, Heng Pu¹, Yuan Liu¹, Shiran Song¹

¹School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, China.
²Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation, Beijing, China.

Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow based on Mask R-CNN. Firstly, through the preprocessing of high spatial resolution remote sensing imagery (HSRRSI) and collecting the artificial samples of outdoor sports venues, the training data set required for object recognition of land cover features was constructed. Secondly, the Mask R-CNN was used as the basic training model to be adapted to cope with outdoor sports venues. Thirdly, the recognition results were compared with the four object-oriented machine learning classification methods in eCognition?. The experiment results of effectiveness verification show that the Mask R-CNN is superior to traditional methods not only in technical procedures but also in outdoor sports venues (football field, basketball court, tennis court and baseball field) recognition results, and it achieves the precision of 0.8927, a recall of 0.9356 and an average precision of 0.9235. Finally, from the aspect of practical engineering application, using and validating the well-trained model, an empirical application experiment was performed on the HSRRSI of Xicheng and Daxing District of Beijing respectively, and the generalization ability of the trained model of Mask R-CNN was thoroughly evaluated.

Instance Recognition, Urban Remote Sensing, High Spatial Resolution Remote Sensing Imagery, Deep Learning, Mask R-CNN

Liu, Y. , Liu, J. , Pu, H. , Liu, Y. and Song, S. (2019) Instance Segmentation of Outdoor Sports Ground from High Spatial Resolution Remote Sensing Imagery Using the Improved Mask R-CNN. International Journal of Geosciences, 10, 884-905. doi: 10.4236/ijg.2019.1010050.