ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation

@article{Pereira2021ChessMixSC,
  title={ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation},
  author={Matheus B. Pereira and Jefersson Alex dos Santos},
  journal={2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)},
  year={2021},
  pages={278-285}
}
  • M. B. Pereira, J. A. D. Santos
  • Published 26 August 2021
  • Computer Science, Environmental Science
  • 2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
Labeling semantic segmentation datasets is a costly and laborious process if compared with tasks like image classification and object detection. This is especially true for remote sensing applications that not only work with extremely high spatial resolution data but also commonly require the knowledge of experts of the area to perform the manual labeling. Data augmentation techniques help to improve deep learning models under the circumstance of few and imbalanced labeled samples. In this work… 
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