论文标题
RCDT：使用变压器的关系遥感变更检测
RCDT: Relational Remote Sensing Change Detection with Transformer
论文作者
论文摘要
基于深度学习的变更检测方法，由于它们从图像中获得丰富功能的强大能力，因此获得了广泛的注意力。但是，现有的基于AI的CD方法在很大程度上依赖于三个功能增强模块，即语义增强，注意机制和对应性增强。这些模块的堆叠导致了巨大的模型复杂性。为了将这三个模块统一为简单的管道，我们引入了关系变更检测变压器（RCDT），这是一个新颖而简单的框架，用于遥感变更检测任务。提出的RCDT由三个主要组件组成，这是一个重量分布的暹罗主链，以获得双期特征，一个关系跨注意模块（RCAM），该模块（RCAM）实现了偏移的交叉注意，以获得双向关系感知的特征，并具有特征约束模块（FCM），以实现最终的精制预测，并获得了高分辨率约束的最终预测。对四个不同公共可用数据集进行的广泛实验表明，与其他竞争方法相比，我们提出的RCDT表现出卓越的变化检测性能。预计这项研究的其他，方法论和实验知识将有益于涉及交叉注意机制的未来变化检测工作。
Deep learning based change detection methods have received wide attentoion, thanks to their strong capability in obtaining rich features from images. However, existing AI-based CD methods largely rely on three functionality-enhancing modules, i.e., semantic enhancement, attention mechanisms, and correspondence enhancement. The stacking of these modules leads to great model complexity. To unify these three modules into a simple pipeline, we introduce Relational Change Detection Transformer (RCDT), a novel and simple framework for remote sensing change detection tasks. The proposed RCDT consists of three major components, a weight-sharing Siamese Backbone to obtain bi-temporal features, a Relational Cross Attention Module (RCAM) that implements offset cross attention to obtain bi-temporal relation-aware features, and a Features Constrain Module (FCM) to achieve the final refined predictions with high-resolution constraints. Extensive experiments on four different publically available datasets suggest that our proposed RCDT exhibits superior change detection performance compared with other competing methods. The therotical, methodogical, and experimental knowledge of this study is expected to benefit future change detection efforts that involve the cross attention mechanism.
