探索遥感影像洪水检测中的数据增强策略，提升深度学习模型精度。
Remote Sensing Imagery for Flood Detection: Exploration of Augmentation Strategies
- 对比多种数据增强方法，包括光学畸变等复杂技术
- 在BlessemFlood21数据集上验证增强效果，提升分割网络性能
- 适用于遥感图像洪水监测，对实际灾害响应有实用价值
洪水在全球范围内造成严重问题，快速有效的应对需要准确及时的受灾区域信息。利用遥感影像进行精确洪水检测需特定检测方法。通常采用深度神经网络，在特定数据集上训练。针对RGB影像中的河流洪水检测，本文使用BlessemFlood21数据集，探索从基础到复杂的数据增强策略，包括光学畸变等技术。通过识别有效策略，旨在优化当前最先进的深度学习分割网络的训练过程。
原文摘要 · Abstract (English)
Floods cause serious problems around the world. Responding quickly and effectively requires accurate and timely information about the affected areas. The effective use of Remote Sensing images for accurate flood detection requires specific detection methods. Typically, Deep Neural Networks are employed, which are trained on specific datasets. For the purpose of river flood detection in RGB imagery, we use the BlessemFlood21 dataset. We here explore the use of different augmentation strategies, ranging from basic approaches to more complex techniques, including optical distortion. By identifying effective strategies, we aim to refine the training process of state-of-the-art Deep Learning segmentation networks.
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