Abstract
Recent years have witnessed the remarkable success of deep learning in remote sensing image interpretation, driven by the availability of large-scale benchmark datasets. However, this reliance on massive training data also brings two major challenges: (1) high storage and computational costs, and (2) the risk of data leakage, especially when sensitive categories are involved. To address these challenges, this study introduces the concept of dataset distillation into the field of remote sensing image interpretation for the first time. Specifically, we train a text-to-image diffusion model to condense a large-scale remote sensing dataset into a compact and representative distilled dataset. To improve the discriminative quality of the synthesized samples, we propose a classifier-driven guidance by injecting a classification consistency loss from a pre-trained model into the diffusion training process. Besides, considering the rich semantic complexity of remote sensing imagery, we further perform latent space clustering on training samples to select representative and diverse prototypes as visual style guidance, while using a visual language model to provide aggregated text descriptions. Experiments on three high-resolution remote sensing scene classification benchmarks show that the proposed method can distill realistic and diverse samples for downstream model training. Code and pre-trained models are available online (this https URL).
Abstract (translated)
近年来，深度学习在遥感图像解读领域取得了显著的成功，这主要得益于大规模基准数据集的可用性。然而，对大量训练数据的高度依赖也带来了两大挑战：一是高昂的存储和计算成本，二是当涉及敏感类别时存在数据泄露的风险。为了解决这些问题，本研究首次将数据集蒸馏的概念引入遥感图像解读领域。具体而言，我们通过训练文本到图像扩散模型来将大规模遥感数据集浓缩成一个紧凑且具有代表性的精炼数据集。 为了提高合成样本的判别质量，我们提出了一种由分类器驱动的指导方法，在扩散训练过程中注入预训练模型中的分类一致性损失。此外，考虑到遥感影像丰富的语义复杂性，我们在训练样本上执行潜在空间聚类以选择代表性且多样化的原型作为视觉风格引导，并使用视觉语言模型提供聚合的文字描述。 在三个高分辨率遥感场景分类基准上的实验表明，所提出的方法能够为下游模型训练蒸馏出真实且多样的样本。代码和预训练模型可在在线平台获取（此链接）。
URL
https://arxiv.org/abs/2601.15829
