Object-Based Augmentation Improves Quality of Remote Sensing Semantic Segmentation. (arXiv:2105.05516v2 [cs.CV] UPDATED)

Today deep convolutional neural networks (CNNs) push the limits for most
computer vision problems, define trends, and set state-of-the-art results. In
remote sensing tasks such as object detection and semantic segmentation, CNNs
reach the SotA performance. However, for precise performance, CNNs require much
high-quality training data. Rare objects and the variability of environmental
conditions strongly affect prediction stability and accuracy. To overcome these
data restrictions, it is common to consider various approaches including data
augmentation techniques. This study focuses on …

arxiv augmentation quality remote segmentation semantic sensing