Change detection aims to identify remote sense object changes by analyzing data between bitemporal image pairs. Due to the large temporal and spatial span of data collection in change detection image pairs, there are often a significant amount of task-specific and task-agnostic noise. Previous effort has focused excessively on denoising, with this goes a great deal of loss of fine-grained information. In this paper, we revisit the importance of fine-grained features in change detection and propose a series of operations for fine-grained information compensation and noise decoupling (FINO). First, the context is utilized to compensate for the fine-grained information in the feature space. Next, a shape-aware and a brightness-aware module are designed to improve the capacity for representation learning. The shape-aware module guides the backbone for more precise shape estimation, guiding the backbone network in extracting object shape features. The brightness-aware module learns a overall brightness estimation to improve the model's robustness to task-agnostic noise. Finally, a task-specific noise decoupling structure is designed as a way to improve the model's ability to separate noise interference from feature similarity. With these training schemes, our proposed method achieves new state-of-the-art (SOTA) results in multiple change detection benchmarks. The code will be made available.

翻译：变化检测旨在通过分析双时相图像对之间的数据来识别遥感对象的变化。由于变化检测图像对数据采集的时间与空间跨度较大，通常存在大量任务相关与任务无关的噪声。以往研究过度侧重于去噪处理，导致细粒度信息大量丢失。本文重新审视细粒度特征在变化检测中的重要性，提出了一系列用于细粒度信息补偿与噪声解耦的操作（FINO）。首先，利用上下文信息在特征空间中对细粒度信息进行补偿。其次，设计了形状感知模块与亮度感知模块以提升表征学习能力：形状感知模块通过引导骨干网络进行更精确的形状估计，从而提取对象形状特征；亮度感知模块通过学习整体亮度估计，提升模型对任务无关噪声的鲁棒性。最后，设计了一种任务相关噪声解耦结构，以增强模型从特征相似性中分离噪声干扰的能力。通过上述训练方案，我们提出的方法在多个变化检测基准测试中取得了最新的最优性能。相关代码将公开提供。