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 the development and testing of object-based augmentation. The practical usefulness of the developed augmentation technique is shown in the remote sensing domain, being one of the most demanded ineffective augmentation techniques. We propose a novel pipeline for georeferenced image augmentation that enables a significant increase in the number of training samples. The presented pipeline is called object-based augmentation (OBA) and exploits objects' segmentation masks to produce new realistic training scenes using target objects and various label-free backgrounds. We test the approach on the buildings segmentation dataset with six different CNN architectures and show that the proposed method benefits for all the tested models. We also show that further augmentation strategy optimization can improve the results. The proposed method leads to the meaningful improvement of U-Net model predictions from 0.78 to 0.83 F1-score.
翻译:今天的深层神经神经网络(CNNs)推动大多数计算机视觉问题的极限,定义趋势,并确定最新结果。在物体探测和语义分割等遥感任务中,CNN达到SotA性能。但是,为了精确的性能,CNN需要高质量的培训数据。很少的物体和环境条件的变异性对预测的稳定性和准确性产生强烈的影响。为了克服这些数据限制,常见的做法是考虑各种办法,包括数据增强技术。本研究侧重于基于对象的扩增的开发和测试。开发的增强技术的实际效用在遥感领域显示,这是最需要的无效的增强技术之一。我们提出了地理参照图像增强的新管道,使培训样本数量大幅度增加。所提出的管道称为基于对象的增强(OBA)并利用物体的分层遮罩,利用目标物体和各种无标签背景产生新的现实的培训场景。我们用六个不同的CNN结构测试建筑分解数据集的方法,并显示所有测试模型的拟议方法的效益。我们还提出了一个新的连接模型。我们展示了从U-0.8号模型到U-0.8号的升级战略的进一步改进。