Automated change detection in remote sensing imagery is critical for urban management, environmental monitoring, and disaster assessment. While deep learning models have advanced this field, they often struggle with challenges like low sensitivity to small objects and high computational costs. This paper presents SCA-Net, an enhanced architecture built upon the Change-Agent framework for precise building and road change detection in bi-temporal images. Our model incorporates several key innovations: a novel Difference Pyramid Block for multi-scale change analysis, an Adaptive Multi-scale Processing module combining shape-aware and high-resolution enhancement blocks, and multi-level attention mechanisms (PPM and CSAGate) for joint contextual and detail processing. Furthermore, a dynamic composite loss function and a four-phase training strategy are introduced to stabilize training and accelerate convergence. Comprehensive evaluations on the LEVIR-CD and LEVIR-MCI datasets demonstrate SCA-Net's superior performance over Change-Agent and other state-of-the-art methods. Our approach achieves a significant 2.64% improvement in mean Intersection over Union (mIoU) on LEVIR-MCI and a remarkable 57.9% increase in IoU for small buildings, while reducing the training time by 61%. This work provides an efficient, accurate, and robust solution for practical change detection applications.

翻译：遥感影像的自动化变化检测对于城市管理、环境监测和灾害评估至关重要。尽管深度学习模型已推动该领域发展，但它们仍常面临对小目标敏感性低和计算成本高等挑战。本文提出了SCA-Net，这是一种基于Change-Agent框架的增强型架构，用于双时相影像中精确的建筑与道路变化检测。我们的模型融合了多项关键创新：一种用于多尺度变化分析的新型差分金字塔模块、一个结合形状感知与高分辨率增强模块的自适应多尺度处理模块，以及用于联合上下文与细节处理的多层次注意力机制（PPM与CSAGate）。此外，引入了动态复合损失函数和四阶段训练策略，以稳定训练并加速收敛。在LEVIR-CD和LEVIR-MCI数据集上的综合评估表明，SCA-Net的性能优于Change-Agent及其他最先进方法。我们的方法在LEVIR-MCI数据集上实现了平均交并比（mIoU）2.64%的显著提升，小型建筑的IoU更是大幅提高了57.9%，同时训练时间减少了61%。这项工作为实际变化检测应用提供了一种高效、准确且鲁棒的解决方案。