Open-pit mine change detection (CD) in high-resolution (HR) remote sensing images plays a crucial role in mineral development and environmental protection. Significant progress has been made in this field in recent years, largely due to the advancement of deep learning techniques. However, existing deep-learning-based CD methods encounter challenges in effectively integrating neighborhood and scale information, resulting in suboptimal performance. Therefore, by exploring the influence patterns of neighborhood and scale information, this paper proposes an Integrated Neighborhood and Scale Information Network (INSINet) for open-pit mine CD in HR remote sensing images. Specifically, INSINet introduces 8-neighborhood-image information to acquire a larger receptive field, improving the recognition of center image boundary regions. Drawing on techniques of skip connection, deep supervision, and attention mechanism, the multi-path deep supervised attention (MDSA) module is designed to enhance multi-scale information fusion and change feature extraction. Experimental analysis reveals that incorporating neighborhood and scale information enhances the F1 score of INSINet by 6.40%, with improvements of 3.08% and 3.32% respectively. INSINet outperforms existing methods with an Overall Accuracy of 97.69%, Intersection over Union of 71.26%, and F1 score of 83.22%. INSINet shows significance for open-pit mine CD in HR remote sensing images.

翻译：高分辨率遥感影像中的露天矿变化检测在矿产开发与环境保护中具有关键作用。近年来，得益于深度学习技术的进步，该领域取得了显著进展。然而，现有基于深度学习的露天矿变化检测方法在有效整合邻域与尺度信息方面仍面临挑战，导致检测性能欠佳。为此，通过探索邻域与尺度信息的影响模式，本文提出了一种面向高分辨率遥感影像露天矿变化检测的邻域与尺度信息集成网络。具体而言，该网络引入8邻域图像信息以获取更大感受野，从而提升中心图像边界区域的识别能力。借鉴跳跃连接、深度监督与注意力机制等技术，设计了多路径深度监督注意力模块，用以增强多尺度信息融合与变化特征提取。实验分析表明，融合邻域与尺度信息使网络F1分数提升6.40%，其中邻域信息与尺度信息的贡献分别为3.08%和3.32%。该网络以总体精度97.69%、交并比71.26%和F1分数83.22%的性能优于现有方法，对高分辨率遥感影像露天矿变化检测具有重要价值。