Instance Segmentation of Outdoor Sports Ground from High Spatial Resolution Remote Sensing Imagery Using the Improved Mask R-CNN
Open Access
- 1 January 2019
- journal article
- research article
- Published by Scientific Research Publishing, Inc. in International Journal of Geosciences
- Vol. 10 (10), 884-905
- https://doi.org/10.4236/ijg.2019.1010050
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.Keywords
This publication has 18 references indexed in Scilit:
- Car Detection from Low-Altitude UAV Imagery with the Faster R-CNNJournal of Advanced Transportation, 2017
- Towards better exploiting convolutional neural networks for remote sensing scene classificationPattern Recognition, 2017
- Application of KNN and Semi-Empirical Models for Prediction of Polycyclic Aromatic Hydrocarbons Solubility in Supercritical Carbon DioxidePolycyclic Aromatic Compounds, 2016
- Object detection in VHR optical remote sensing images via learning rotation-invariant HOG featurePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
- Fast R-CNNPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Going deeper with convolutionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
- Rich Feature Hierarchies for Accurate Object Detection and Semantic SegmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imageryGeocarto International, 2013