ClearSCD 模型:在高空间分辨率遥感图像中全面利用语义和变化关系进行语义变化检测

IF 10.6 1区 地球科学 Q1 GEOGRAPHY, PHYSICAL
Kai Tang , Fei Xu , Xuehong Chen , Qi Dong , Yuheng Yuan , Jin Chen
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引用次数: 0

摘要

地球一直在经历着持续的人为和自然变化。高空间分辨率(HSR)遥感图像为在全球范围内准确揭示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 方法很少模拟语义与变化之间的依赖关系,导致检测复杂地表变化的精度不理想。针对这一局限性,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与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变化与二进制变化信息之间的相关性;第三个模块使用语义增强对比学习模块,以提高其他两个模块的性能。我们在基准数据集和真实世界场景(名为 LsSCD 数据集)上测试了 ClearSCD 与最先进方法的性能对比,结果表明 ClearSCD 在 mIoUsc 指标上比其他方法高出 1.23% 到 19.34%。此外,消融实验证明了三个创新模块对提高性能的独特贡献。在不同地貌条件下的高计算效率和稳健性能表明,ClearSCD 是利用 HSR 图像检测详细地表变化的实用工具。代码和 LsSCD 数据集见 https://github.com/tangkai-RS/ClearSCD。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The ClearSCD model: Comprehensively leveraging semantics and change relationships for semantic change detection in high spatial resolution remote sensing imagery

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 mIoUsc 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 https://github.com/tangkai-RS/ClearSCD.

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来源期刊
ISPRS Journal of Photogrammetry and Remote Sensing
ISPRS Journal of Photogrammetry and Remote Sensing 工程技术-成像科学与照相技术
CiteScore
21.00
自引率
6.30%
发文量
273
审稿时长
40 days
期刊介绍: The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive. P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields. In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.
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