掩码近似网络：一种用于遥感变化描述的新型扩散模型方法
Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning
AI总结：
本文提出掩码近似网络，利用扩散模型学习数据分布并结合频域噪声滤波，通过多尺度变化检测与频率引导复数滤波模块，提升遥感变化描述性能。
摘要：
遥感图像变化描述是遥感处理领域中一项创新的多模态任务。该任务不仅有助于检测地表状态的变化，还能提供对这些变化的全面描述，从而提高人类可解释性和交互性。当前的深度学习方法通常采用三阶段框架，包括特征提取、特征融合和变化定位，然后进行文本生成。大多数方法侧重于设计复杂的网络模块，但缺乏坚实的理论指导，而是依赖大量的经验实验和网络组件的迭代调优。这种经验驱动的设计范式可能导致过拟合和设计瓶颈，从而限制模型的泛化能力和适应性。为了解决这些局限性，本文提出了一种范式转变，即利用扩散模型进行数据分布学习，并通过频域噪声滤波加以强化，为多模态遥感变化描述提供一种具有理论依据且实际有效的解决方案。所提出的方法主要包括一个简单的多尺度变化检测模块，其输出特征随后由精心设计的扩散模型进行细化。此外，我们引入了一个频率引导的复数滤波模块，通过在扩散过程中管理高频噪声来提升模型性能。我们在多个遥感变化检测与描述数据集上验证了所提方法的有效性，展示了其相较于现有技术的优越性能。代码将在 \href{https://github.com/sundongwei}{MaskApproxNet} 上提供。
英文摘要：
Remote sensing image change description represents an innovative multimodal task within the realm of remote sensing processing.This task not only facilitates the detection of alterations in surface conditions, but also provides comprehensive descriptions of these changes, thereby improving human interpretability and interactivity.Current deep learning methods typically adopt a three stage framework consisting of feature extraction, feature fusion, and change localization, followed by text generation. Most approaches focus heavily on designing complex network modules but lack solid theoretical guidance, relying instead on extensive empirical experimentation and iterative tuning of network components. This experience-driven design paradigm may lead to overfitting and design bottlenecks, thereby limiting the model's generalizability and adaptability.To address these limitations, this paper proposes a paradigm that shift towards data distribution learning using diffusion models, reinforced by frequency-domain noise filtering, to provide a theoretically motivated and practically effective solution to multimodal remote sensing change description.The proposed method primarily includes a simple multi-scale change detection module, whose output features are subsequently refined by a well-designed diffusion model.Furthermore, we introduce a frequency-guided complex filter module to boost the model performance by managing high-frequency noise throughout the diffusion process. We validate the effectiveness of our proposed method across several datasets for remote sensing change detection and description, showcasing its superior performance compared to existing techniques. The code will be available at \href{https://github.com/sundongwei}{MaskApproxNet}.
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。
