Instance Segmentation of Outdoor Sports Ground from High Spatial Resolution Remote Sensing Imagery Using the Improved Mask R-CNN
- 1 School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, China
- 2 Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation, Beijing, China
- 3 School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, China
- 4 School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, China
- 5 School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing, China
Abstract
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.
- Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A. (2015) Going Deeper with Convolutions. Conference on Computer Vision and Pattern Recognition, Boston, 7-12 June 2015, 2-8. https://doi.org/10.1109/CVPR.2015.7298594
- Girshick, R., Donahue, J., Darrell, T. and Malik, J. (2014) Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Conference on Computer Vision and Pattern Recognition, Columbus, 23-28 June 2014, 580-587. https://doi.org/10.1109/CVPR.2014.81
- Girshick, R. (2015) Fast R-CNN. In: Proceedings of the 2015 IEEE International Conference on Computer Vision, IEEE Computer Society, Washington DC, 1440-1448. https://doi.org/10.1109/ICCV.2015.169
- He, K.M., Zhang, X.Y., Ren, S.Q. and Sun, J. (2014) Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. Transactions on Pattern Analysis & Machine Intelligence, 37, 1904-1916. https://doi.org/10.1109/TPAMI.2015.2389824
- Ren, S.Q., He, K.M., Girshick, R. and Sun, J. (2015) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39, 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031
- Xu, Y.Z., Yu, G.Z., Wang, Y.P., Wu, X.K. and Ma, Y.L. (2017) Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN. Journal of Advanced Transportation, 2017, Article ID: 2823617. https://doi.org/10.1155/2017/2823617
- Sun, X.D., Wu, P.C. and Hoi, S.C.H. (2018) Face Detection Using Deep Learning: An Improved Faster RCNN Approach. Neurocomputing, 299, 42-50.
- He, K.M., Gkioxari, G., Dollár, P. and Girshick, R. (2018) Mask R-CNN. IEEE International Conference on Computer Vision, Venice, 22-29 October 2017, 1. https://doi.org/10.1109/TPAMI.2018.2844175
- Zhao, K., Kang, J., Jung, J. and Sohn, G. (2018) Building Extraction from Satellite Images Using Mask R-CNN with Building Boundary Regularization. Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, 18-22 June 2018, 247-251.
- Ji, S.P., Wei, S.Q. and Lu, M. (2018) Fully Convolutional Networks for Multisource Building Extraction from an Open Aerial and Satellite Imagery Data Set. IEEE Transactions on Geoscience and Remote Sensing, 57, 574-586.
- Song, S.R., Liu, J.H., Pu, H., Liu, Y. and Luo, J.Y. (2019) The Comparison of Fusion Methods for HSRRSI Considering the Effectiveness of Land Cover (Features) Object Recognition Based on Deep Learning. Remote Sensing, 11, 1435. https://doi.org/10.3390/rs11121435