发表机构
The Ohio State University; Boston University; Utah State University; Universidad de Antioquia（俄亥俄州立大学; 波士顿大学; 犹他州立大学; 安蒂奥基亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对野火图像语义分割中标注稀缺的问题，提出上下文感知的集中式复制粘贴数据增强方法，通过限制火灾放置到语义有效区域并匹配灰烬-植被组成，提升数据真实性与分割性能。
AI 中文摘要
为基于深度学习的图像分割生成精确标注既昂贵又费力。这一挑战在野火应用中尤为明显，由于收集和标注动态火灾场景的困难，准确标注的数据集十分稀缺。为解决此问题，我们先前的工作引入了用于野火图像语义分割的集中式复制粘贴数据增强（CCPDA）方法，该方法通过将源图像中的火灾簇随机粘贴到目标图像上来生成人工训练样本。然而，随机放置可能产生上下文不真实的场景，例如火在沥青上燃烧。本文提出了一种上下文感知策略，专门用于提高小型多类野火数据集的数据质量和真实性，确保增强样本保持上下文意义。所提方法将火灾放置限制在语义有效的目标区域，并选择其灰烬-植被组成与源上下文最匹配的位置。该方法保留了目标图像中现有的火灾区域，防止不真实的放置，并通过生成类似真实野火场景的图像来保持上下文准确性。我们通过数值分析以及使用基于加权和的多目标优化（MOO）方法与其他增强方法进行比较，评估了上下文感知CCPDA策略。结果证实，上下文感知数据增强策略可提高分割性能和上下文真实性，优于其他增强程序。
英文摘要
Producing accurate annotations for deep learning based image segmentation is both costly and labor intensive. This challenge is especially evident in wildland fire applications, where accurately labeled datasets are scarce due to the difficulty of collecting and annotating dynamic fire scenes. To address this problem, our previous work introduced the Centralized Copy-Paste Data Augmentation (CCPDA) method for semantic segmentation of wildland fire imagery, which generates artificial training samples by randomly pasting fire clusters from source images onto target images. However, random placement can produce contextually unrealistic scenes, such as fire burning on asphalt. In this paper, we present a context-aware strategy designed specifically to improve data quality and realism in small multiclass wildland fire datasets, ensuring that augmented samples remain contextually meaningful. The proposed method restricts fire placement to semantically valid target regions and selects the location whose Ash-Vegetation composition most closely matches the source context. This approach preserves existing fire regions in the target image, prevents unrealistic placements, and maintains contextual accuracy by generating images that resemble real wildland fire scenes. We evaluate the Context-Aware CCPDA strategy through numerical analysis and comparisons with other augmentation methods by a weighted sum-based multi-objective optimization (MOO) approach. The results confirm that the context-aware data augmentation strategy leads to improved segmentation performance and contextual realism, outperforming other augmentation procedures.
Comments14 pages, 9 figures
