arXiv 2507.049152025-07-07cs.CV

利用自监督特征实现无人机航拍图像中洪水淹没区域的高效识别

Leveraging Self-Supervised Features for Efficient Flooded Region Identification in UAV Aerial Images

Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India（电子与通信工程系，Manipal理工学院，Manipal高等教育学院，Manipal，印度）

Dibyabha Deb, Ujjwal Verma

备注：: 13 Pages, 4 Figures

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AI总结：

本文提出两种融合DINOv2自监督特征与编码器-解码器骨干的分割方法，验证其在无人机航拍图像洪水识别中的泛化能力，显著降低标注依赖并实现高精度分割。

摘要：

识别受灾区域是有效管理和规划救援工作的关键步骤。与传统的人工评估灾后损失方法不同，分析无人机（UAV）图像为评估损失提供了一种客观且可靠的方式。过去，分割技术已被用于识别无人机航拍图像中的洪水后损失。然而，大多数监督学习方法依赖于人工标注的数据集。实际上，标注图像是一项耗时且容易出错的任务，需要领域专业知识。本研究聚焦于利用自监督特征来准确识别无人机航拍图像中的洪水淹没区域。本文提出了两种基于编码器-解码器的分割方法，将DINOv2学习到的视觉特征与传统编码器骨干网络相融合。本研究考察了自监督特征在无人机航拍图像上的泛化能力。具体而言，我们评估了在非航拍图像上训练的DINOv2模型特征用于航拍图像分割的有效性，并注意到两类图像之间视角的显著差异。我们的结果表明，DINOv2在自然图像上的自监督预训练能够生成可迁移的、通用的视觉特征，从而简化了航拍分割工作流的开发过程。通过将这些特征作为基础，我们显著减少了对劳动密集型人工标注流程的依赖，从而在有限的标注航拍数据条件下实现高精度分割。

英文摘要：

Identifying regions affected by disasters is a vital step in effectively managing and planning relief and rescue efforts. Unlike the traditional approaches of manually assessing post-disaster damage, analyzing images of Unmanned Aerial Vehicles (UAVs) offers an objective and reliable way to assess the damage. In the past, segmentation techniques have been adopted to identify post-flood damage in UAV aerial images. However, most of these supervised learning approaches rely on manually annotated datasets. Indeed, annotating images is a time-consuming and error-prone task that requires domain expertise. This work focuses on leveraging self-supervised features to accurately identify flooded regions in UAV aerial images. This work proposes two encoder-decoder-based segmentation approaches, which integrate the visual features learned from DINOv2 with the traditional encoder backbone. This study investigates the generalization of self-supervised features for UAV aerial images. Specifically, we evaluate the effectiveness of features from the DINOv2 model, trained on non-aerial images, for segmenting aerial images, noting the distinct perspectives between the two image types. Our results demonstrate that DINOv2's self-supervised pretraining on natural images generates transferable, general-purpose visual features that streamline the development of aerial segmentation workflows. By leveraging these features as a foundation, we significantly reduce reliance on labor-intensive manual annotation processes, enabling high-accuracy segmentation with limited labeled aerial data.

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