交叉地球:地理空间视觉基础模型用于域普用遥感语义细分的域普用远程传感.
IEEE transactions on pattern analysis and machine intelligence
|December 29, 2025
概括
介绍CrossEarth,这是第一个远程传感域泛化 (RSDG) 语义细分的视觉基础模型. 它在新的基准上实现了卓越的跨领域通用化,优于现有方法.
科学领域:
- 地球和太空科学 地球和太空科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 远程传感 (RS) 图像由于位置,波长和传感器类型的变化而表现出显著的域间隙.
- 现有的跨域方法往往侧重于预定义域的域调整 (DA),而不是对未见的场景的概括.
- 对于语义细分的远程传感域泛化 (RSDG) 的研究是有限的,目前的模型在不同的未见域中表现不佳.
研究的目的:
- 引入第一个专门为远程传感域泛化 (RSDG) 语义细分设计的视觉基础模型.
- 解决当前模型的局限性,将其推广到多样化和未见的遥感场景.
- 建立一个全面的基准来评估RSDG语义细分模型.
主要方法:
- 开发了CrossEarth,这是一个新的视觉基础模型,用于RSDG语义细分.
- 实现了数据级地球风格注入管道,以增强域名可变性.
- 利用模型级多任务培训管道来提高概括能力.
- 策划了一个新的RSDG基准,包含32个语义细分场景,跨越多种条件.
主要成果:
- 交叉地球展示了强大的跨域概括能力.
- 拟议的地球式注入和多任务训练管道显著提高了性能.
- 对新基准进行了广泛的实验,证实了CrossEarth在最先进的方法上的优势.
- 该基准为未来的RSDG模型开发提供了可靠的评估.
结论:
- 交叉地球是第一个有效的视觉基础模型,用于RSDG语义细分.
- 该模型的架构和训练策略使得它能够在各种遥感领域进行优越的概括.
- 新编制的基准标准有助于全面评估和推进RSDG研究.
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