探索通过几何估计实现真实世界变化检测的可泛化预训练
Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation
AI总结：
本文提出MatchCD框架，利用自监督对比学习和零样本能力，通过几何估计同时处理图像配准与变化检测，无需手动预配准，可直接处理大尺度图像，在复杂场景中验证了有效性。
摘要：
作为地球观测系统中的一项基本流程，变化检测（CD）旨在揭示观测区域的时空演变。现有变化检测算法的一个关键前提是通过精细配准实现多时相图像之间的地理参考对齐。然而，在大多数真实世界场景中，原始图像之间需要事先进行手动配准，这显著增加了变化检测工作流程的复杂性。在本文中，我们提出了一种受自监督启发的、带有几何估计的变化检测框架，称为“MatchCD”。具体而言，所提出的MatchCD框架利用零样本能力，通过自监督对比表示来优化编码器，该编码器在下游图像配准和变化检测中被复用，以同时处理双时相不对齐和物体变化问题。此外，与传统变化检测需要将全帧图像分割成小块不同，我们的MatchCD框架可以直接处理原始大尺度图像（例如，6K*4K分辨率），并具有令人满意的性能。在多个具有显著几何畸变的复杂场景中的性能证明了我们提出框架的有效性。
英文摘要：
As an essential procedure in earth observation system, change detection (CD) aims to reveal the spatial-temporal evolution of the observation regions. A key prerequisite for existing change detection algorithms is aligned geo-references between multi-temporal images by fine-grained registration. However, in the majority of real-world scenarios, a prior manual registration is required between the original images, which significantly increases the complexity of the CD workflow. In this paper, we proposed a self-supervision motivated CD framework with geometric estimation, called "MatchCD". Specifically, the proposed MatchCD framework utilizes the zero-shot capability to optimize the encoder with self-supervised contrastive representation, which is reused in the downstream image registration and change detection to simultaneously handle the bi-temporal unalignment and object change issues. Moreover, unlike the conventional change detection requiring segmenting the full-frame image into small patches, our MatchCD framework can directly process the original large-scale image (e.g., 6K*4K resolutions) with promising performance. The performance in multiple complex scenarios with significant geometric distortion demonstrates the effectiveness of our proposed framework.
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。
