一个基于深度学习的远程传感图像变化检测平台,整合众包和主动学习.
Zhibao Wang1,2, Jie Zhang1, Lu Bai3
1School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
本研究介绍了一种由人工智能驱动的框架,用于自动遥感图像变化检测. 它使用众包和积极学习来提高土地覆盖监测模型的准确性和效率.
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:
- 遥感变化检测对于土地覆盖面的监测至关重要,但传统方法缓慢且昂贵.
- 现有的方法在有限的注释数据上扎,阻碍了模型的概括和及时分析.
- 人工智能为自动化和提高变化检测流程效率提供了潜力.
研究的目的:
- 开发一个与众包协作框架集成的自动变更检测模型.
- 为了应对在遥感变化检测中,注释数据不足和模型概括性较弱的挑战.
- 提高自然资源部门对土地覆盖变化监测的效率和有效性.
主要方法:
- 提出了一个自动变更检测模型与众包协作框架相结合.
- 实现了人类在循环中的技术和积极学习,以实现智能解释.
- 开发了一个众包质量控制模型,以确保注释准确性和注释者资格.
- 创建了一个原型平台,集成注释,质量控制和变更检测应用程序.
主要成果:
- 人机协作智能口译方法显著改进了传统的手动口译.
- 该框架有效地结合了专业领域的知识,降低了数据注释成本.
- 众包质量控制模型确保了可靠的注释结果和注释器性能.
- 开发的平台为土地覆盖变化监测提供了高效和有效的解决方案.
结论:
- 拟议的AI驱动框架为远程传感图像变化检测提供了低成本,高效的解决方案.
- 这种方法克服了数据稀缺问题,提高了模型的概括性和性能.
- 综合平台为自然资源部门提供了用于有效监测土地覆盖面的先进工具.
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