Very-high-resolution (VHR) remote sensing (RS) image change detection (CD) has been a challenging task for its very rich spatial information and sample imbalance problem. In this paper, we have proposed a hierarchical change guiding map network (HCGMNet) for change detection. The model uses hierarchical convolution operations to extract multiscale features, continuously merges multi-scale features layer by layer to improve the expression of global and local information, and guides the model to gradually refine edge features and comprehensive performance by a change guide module (CGM), which is a self-attention with changing guide map. Extensive experiments on two CD datasets show that the proposed HCGMNet architecture achieves better CD performance than existing state-of-the-art (SOTA) CD methods.

翻译：超高分辨率遥感影像变化检测因其极其丰富的空间信息和样本不平衡问题而一直是一项具有挑战性的任务。本文提出了一种用于变化检测的分层变化引导图网络（HCGMNet）。该模型利用分层卷积操作提取多尺度特征，通过逐层连续融合多尺度特征以增强全局和局部信息的表达能力，并借助变化引导模块（CGM）引导模型逐步细化边缘特征和综合性能，该模块是一种带有变化引导图的自注意力机制。在两个变化检测数据集上的大量实验表明，所提出的HCGMNet架构比现有的最优变化检测方法取得了更好的性能。