SEMANTIC SEGMENTATION OF AERIAL IMAGERY VIA MULTI-SCALE SHUFFLING CONVOLUTIONAL NEURAL NETWORKS WITH DEEP SUPERVISION

6 citations as recorded by crossref.

1. Automatic labelling for semantic segmentation of VHR satellite images: Application of airborne laser scanner data and object-based image analysis K. Karila et al. 10.1016/j.ophoto.2023.100046
2. Intelligent Mapping of Urban Forests from High-Resolution Remotely Sensed Imagery Using Object-Based U-Net-DenseNet-Coupled Network S. He et al. 10.3390/rs12233928
3. Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis B. Neupane et al. 10.3390/rs13040808
4. Change detection techniques for remote sensing applications: a survey A. Asokan & J. Anitha 10.1007/s12145-019-00380-5
5. Research Contribution and Comprehensive Review towards the Semantic Segmentation of Aerial Images Using Deep Learning Techniques P. Anilkumar et al. 10.1155/2022/6010912
6. CCANet: Class-Constraint Coarse-to-Fine Attentional Deep Network for Subdecimeter Aerial Image Semantic Segmentation G. Deng et al. 10.1109/TGRS.2021.3055950