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域适应细分任何卫星图像中跨域水体细分的模型

Lihong Yang^1,2, Pengfei Liu^1,2, Guilong Zhang^1,2

¹Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China.

Journal of imaging

|December 24, 2025

概括

我们开发了DASAM,这是一个适应域的任何细分模型,以改善卫星图像中的水体细分. 这种方法可以在各种卫星数据中增强泛化,而不需要目标域标签.

科学领域:

遥感遥感遥感遥感
计算机视觉计算机视觉
环境监测环境监测

背景情况:

精确的地表水体监测对于环境保护和资源管理至关重要.
当前的卫星图像细分技术往往缺乏跨不同卫星领域的概括性.
域名适应对于强大的水体细分至关重要.

研究的目的:

介绍DASAM,一个适应域的分段任何模型 (SAM),用于跨域水体细分.
增强对各种卫星图像的分段模型的概括能力.
提高水体检测在环境分析中的准确性和稳定性.

主要方法:

DASAM使用一个对比式学习模块来对准图像特征,使得域概括能够在没有目标域注释的情况下实现.
集成了一个提示增强模块和编码器适配器,以捕获细粒度的空间细节和全球上下文.
该模型使用对中国GF-2数据集的实验和对GLH-水和Sentinel-2数据集的跨领域评估进行评估.

主要成果:

与中国GF-2数据集上的现有方法相比,DASAM表现优越.
跨领域的评估证实了DASAM在GLH-水和Sentinel-2数据集上的强烈概括性和稳定性.
拟议的方法有效地解决了跨域水体细分的有限概括的挑战.

结论:

在卫星图像中,DASAM为跨域水体细分提供了强大的解决方案.
该模型的领域适应性方法提高了其适用于大规模和多样化的环境监测任务的适用性.
通过卫星数据,DASAM显示了通过卫星数据提高环境分析准确性的巨大潜力.

关键词:

分段任何模型模型域名适应域名适应图像细分图像细分水体检测水体检测的方法

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