  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.
