GCD-DDPM：一种基于差异特征引导 DDPM 的生成式变化检测模型
GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPM
AI总结：
本文提出 GCD-DDPM，以 DDPM 直接生成变化检测图，并通过差异条件编码器、变分迭代推理和噪声抑制语义增强器提升复杂场景下的细粒度检测性能。
摘要：
近年来，基于深度学习（DL）的方法在双时相变化检测（CD）中展现出巨大潜力。现有基于卷积神经网络（CNN）和 Transformer 的判别式方法依赖判别表征学习来识别变化，却难以挖掘局部与长程上下文依赖。因此，在多样地表场景中获得细粒度且鲁棒的 CD 图仍具挑战。为应对该挑战，本文提出一种名为 GCD-DDPM 的生成式变化检测模型，利用去噪扩散概率模型（DDPM）直接生成 CD 图，而非将每个像素分类为变化或未变化类别。此外，本文设计了差异条件编码器（DCE），通过利用多级差异特征引导 CD 图生成。借助变分推断（VI）过程，GCD-DDPM 能够通过迭代推理过程自适应地重新校准 CD 结果，同时准确区分多样场景中细微且不规则的变化。最后，本文专门设计了基于噪声抑制的语义增强器（NSSE），以缓解来自 CD 编码器的当前步骤变化感知特征表征中的噪声。该精化结果作为注意力图，可在提升 CD 精度的同时引导后续迭代。在四个高分辨率 CD 数据集上的大量实验证实了所提出 GCD-DDPM 的优越性能。本工作代码将在 https://github.com/udrs/GCD 提供。
英文摘要：
Deep learning (DL)-based methods have recently shown great promise in bitemporal change detection (CD). Existing discriminative methods based on Convolutional Neural Networks (CNNs) and Transformers rely on discriminative representation learning for change recognition while struggling with exploring local and long-range contextual dependencies. As a result, it is still challenging to obtain fine-grained and robust CD maps in diverse ground scenes. To cope with this challenge, this work proposes a generative change detection model called GCD-DDPM to directly generate CD maps by exploiting the Denoising Diffusion Probabilistic Model (DDPM), instead of classifying each pixel into changed or unchanged categories. Furthermore, the Difference Conditional Encoder (DCE), is designed to guide the generation of CD maps by exploiting multi-level difference features. Leveraging the variational inference (VI) procedure, GCD-DDPM can adaptively re-calibrate the CD results through an iterative inference process, while accurately distinguishing subtle and irregular changes in diverse scenes. Finally, a Noise Suppression-based Semantic Enhancer (NSSE) is specifically designed to mitigate noise in the current step's change-aware feature representations from the CD Encoder. This refinement, serving as an attention map, can guide subsequent iterations while enhancing CD accuracy. Extensive experiments on four high-resolution CD datasets confirm the superior performance of the proposed GCD-DDPM. The code for this work will be available at https://github.com/udrs/GCD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
