用于乌克兰战区受损农田卫星分析的合成数据增强方法
Synthetic Data Augmentation for Satellite-Based Analysis of Battle-Damaged Agricultural Fields in Ukraine
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- Ukrainian Catholic University（乌克兰天主教大学）
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中文总结 AI 辅助
本研究针对乌克兰受损农田卫星分析的标注数据稀缺问题，采用类别条件GAN与DDPM生成合成农田样本，结合真实数据训练视觉Transformer，使分类性能显著提升，验证了合成卫星图像在数据稀缺地理空间应用的潜力。
中文摘要 AI 辅助
监测战争导致的乌克兰农田损毁，对理解粮食安全威胁、环境稳定及战后恢复至关重要。然而，基于卫星的损毁分析计算机视觉系统的开发受限于标注图像稀缺性，尤其是受损农田的标注数据。本研究探究合成数据增强作为有限且不平衡训练数据下提升分类性能的方法。我们在真实卫星图像上训练类别条件生成对抗网络（GAN）和去噪扩散概率模型（DDPM）架构，用于生成更多被炸与未被炸农田样本。生成图像仅用于训练增强，所有下游评估均在纯真实测试集上进行。我们在多种真实与合成数据配置下训练视觉Transformer分类器，以衡量各生成方法的实际效用。基于平衡DDPM增强的最佳配置，准确率从84%提升至88%，平衡准确率从67%提升至81%，宏F1值从65%提升至78%，样本不足的未被炸类别的召回率从41%提升至69%。这些结果表明，合成卫星图像在受战争影响地区的数据稀缺地理空间应用中具有潜力。
英文摘要
Monitoring war-induced damage to agricultural land in Ukraine is important for understanding threats to food security, environmental stability, and post-war recovery. However, the development of computer-vision systems for satellite-based damage analysis is limited by the scarcity of labeled imagery, especially for damaged agricultural fields. This work investigates synthetic data augmentation as a method for improving classification under limited and imbalanced training data. We train class-conditional Generative Adversarial Network (GAN) and Denoising Diffusion Probabilistic Model (DDPM) architectures on real satellite images and use them to generate additional bombed and not-bombed agricultural-field samples. The generated images are used only for training augmentation, while all downstream evaluation is performed on an exclusively real test set. A Vision Transformer classifier is trained under multiple real and synthetic data configurations to measure the practical utility of each generative approach. The best configuration, based on balanced DDPM augmentation, improves accuracy from 84\% to 88\%, balanced accuracy from 67\% to 81\%, macro F1 from 65\% to 78\%, and recall for the underrepresented not-bombed class from 41\% to 69\%. These results demonstrate the potential of synthetic satellite imagery for data-scarce geospatial applications in war-affected regions.
