The Earth has been undergoing continuous anthropogenic and natural change. High spatial resolution (HSR) remote sensing imagery provides a unique opportunity to accurately reveal these changes on a planetary scale. Semantic change detection (SCD) with HSR imagery has become a common technique for tracking the evolution of land surface types at a semantic level. However, existing SCD methods rarely model the dependency between semantics and changes, resulting in suboptimal accuracy in detecting complicated surface changes. To address this limitation, we propose ClearSCD, a multi-task learning model that leverages the mutual gain relationship between semantics and change through three innovative modules. The first module interprets semantic features at different times into posterior probabilities for surface types to detect binary change information; the second module learns the correlation between surface types over time and the binary change information; a semantic augmented contrastive learning module is used as the third module to improve the performance of the other two modules. We tested ClearSCD’s performance against state-of-the-art methods on benchmark datasets and a real-world scenario (named LsSCD dataset), showing that ClearSCD outperformed the alternatives on mIoU metrics by 1.23% to 19.34%. Furthermore, ablation experiments demonstrated the unique contribution of the three innovative modules to performance improvement. The high computational efficiency and robust performance over diverse landscapes demonstrate that ClearSCD is an operational tool for detecting detailed land surface changes from HSR imagery. Code and LsSCD dataset available at .

中文翻译：

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ClearSCD 模型：综合利用语义和变化关系进行高空间分辨率遥感影像语义变化检测

地球一直在经历持续的人为和自然变化。高空间分辨率（HSR）遥感图像为准确揭示全球范围内的这些变化提供了独特的机会。利用 HSR 图像进行语义变化检测 (SCD) 已成为在语义层面跟踪地表类型演变的常用技术。然而，现有的 SCD 方法很少对语义和变化之间的依赖关系进行建模，导致检测复杂表面变化的准确性不理想。为了解决这一限制，我们提出了 ClearSCD，这是一种多任务学习模型，通过三个创新模块利用语义和变化之间的互利关系。第一个模块将不同时间的语义特征解释为表面类型的后验概率，以检测二进制变化信息；第二个模块学习表面类型随时间的相关性和二进制变化信息；语义增强对比学习模块用作第三个模块，以提高其他两个模块的性能。我们在基准数据集和真实场景（名为 LsSCD 数据集）上针对最先进的方法测试了 ClearSCD 的性能，结果表明 ClearSCD 在 mIoU 指标上的性能优于替代方案 1.23% 至 19.34%。此外，消融实验证明了三个创新模块对性能改进的独特贡献。在不同景观上的高计算效率和强大性能表明 ClearSCD 是一种用于从 HSR 图像检测详细地表变化的操作工具。代码和 LsSCD 数据集可在 .