论文标题
SARAS-NET：比例和关系意识到的暹罗网络用于变更检测
SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection
论文作者
论文摘要
变更检测（CD）旨在在不同时间找到两个图像之间的差异，并输出一个变更图，以表示该区域是否已更改。为了获得更好的结果，许多最先进的方法（SOTA）方法设计了具有强大歧视能力的深度学习模型。但是，这些方法仍然获得较低的性能，因为它们忽略了空间信息和对象之间的变化，从而导致模糊或错误的边界。除此之外，他们还忽略了两个不同图像的交互信息。为了减轻这些问题，我们建议我们的网络，规模和关系感知的暹罗网络（SARAS-NET）来解决这个问题。在本文中，提出了三个模块，其中包括关系感知，比例了解和跨变形器，以更有效地解决场景变化检测的问题。为了验证我们的模型，我们测试了三个公共数据集，包括Levir-CD，WHU-CD和DSFIN，并获得了SOTA准确性。我们的代码可在https://github.com/f64051041/saras-net上找到。
Change detection (CD) aims to find the difference between two images at different times and outputs a change map to represent whether the region has changed or not. To achieve a better result in generating the change map, many State-of-The-Art (SoTA) methods design a deep learning model that has a powerful discriminative ability. However, these methods still get lower performance because they ignore spatial information and scaling changes between objects, giving rise to blurry or wrong boundaries. In addition to these, they also neglect the interactive information of two different images. To alleviate these problems, we propose our network, the Scale and Relation-Aware Siamese Network (SARAS-Net) to deal with this issue. In this paper, three modules are proposed that include relation-aware, scale-aware, and cross-transformer to tackle the problem of scene change detection more effectively. To verify our model, we tested three public datasets, including LEVIR-CD, WHU-CD, and DSFIN, and obtained SoTA accuracy. Our code is available at https://github.com/f64051041/SARAS-Net.
