论文标题
DDPM-CD：将扩散概率模型作为用于变化检测的特征提取器
DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection
论文作者
论文摘要
遥感变化检测对于理解地球表面的动态，促进环境变化的监测，评估人类影响，预测未来趋势并支持决策至关重要。在这项工作中，我们介绍了一种新颖的变更检测方法，可以通过预先训练培训deno的扩散概率模型（DDPM）（在图像合成中使用的一类生成模型）来利用训练过程中的未标记，未标记的遥感图像。 DDPM通过使用马尔可夫链逐渐将训练图像转换为高斯分布来学习训练数据分布。在推断（即采样）期间，它们可以生成各种样品，从高斯噪声开始，从而实现最新的图像合成结果，更接近训练分布。但是，在这项工作中，我们的重点不是图像合成，而是将其用作预训练的特征提取器，用于下游更改检测。具体而言，我们利用预先训练的DDPM以及更改标签的特征表示形式微调了轻量化的分类器。 Experiments conducted on the LEVIR-CD, WHU-CD, DSIFN-CD, and CDD datasets demonstrate that the proposed DDPM-CD method significantly outperforms the existing state-of-the-art change detection methods in terms of F1 score, IoU, and overall accuracy, highlighting the pivotal role of pre-trained DDPM as a feature extractor for downstream applications.我们已经在https://github.com/wgcban/ddpm-cd上提供了代码和预训练的模型
Remote sensing change detection is crucial for understanding the dynamics of our planet's surface, facilitating the monitoring of environmental changes, evaluating human impact, predicting future trends, and supporting decision-making. In this work, we introduce a novel approach for change detection that can leverage off-the-shelf, unlabeled remote sensing images in the training process by pre-training a Denoising Diffusion Probabilistic Model (DDPM) - a class of generative models used in image synthesis. DDPMs learn the training data distribution by gradually converting training images into a Gaussian distribution using a Markov chain. During inference (i.e., sampling), they can generate a diverse set of samples closer to the training distribution, starting from Gaussian noise, achieving state-of-the-art image synthesis results. However, in this work, our focus is not on image synthesis but on utilizing it as a pre-trained feature extractor for the downstream application of change detection. Specifically, we fine-tune a lightweight change classifier utilizing the feature representations produced by the pre-trained DDPM alongside change labels. Experiments conducted on the LEVIR-CD, WHU-CD, DSIFN-CD, and CDD datasets demonstrate that the proposed DDPM-CD method significantly outperforms the existing state-of-the-art change detection methods in terms of F1 score, IoU, and overall accuracy, highlighting the pivotal role of pre-trained DDPM as a feature extractor for downstream applications. We have made both the code and pre-trained models available at https://github.com/wgcban/ddpm-cd
