使用地标物体提取和ANN方法进行气象可见度估计.
Wai-Lun Lo1, Kwok-Wai Wong1, Richard Tai-Chiu Hsung1
1Department of Computer Science, Hong Kong Chu Hai College, 80 Castle Peak Road, Castle Peak Bay, Tuen Mun, New Territories, Hong Kong, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究引入了一种自动化方法来选择关键图像区域,以提高可见度估计的准确性. 通过使用具有里程碑意义的物体提取,该方法提高了环境监测和空气质量评估.
科学领域:
- 环境科学 环境科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 可见度估计对于环境监测至关重要,包括天气和空气污染.
- 传统方法依赖于气象规律或图像分析,但对图像质量和噪音敏感.
- 现有的AI方法使用图像特征,但通常需要手动选择相关图像子区域.
研究的目的:
- 开发一种自动化方法来选择有效的子区域以进行可见度估计.
- 使用人工智能提高可见度估计的准确性和效率.
- 为了减少图像数据中的冗余信息,以便更强大的AI模型培训.
主要方法:
- 提出了一种使用具有里程碑意义的物体提取技术的自动有效的分区域选择方法.
- 从已识别的地标对象 (LMO) 亚区域提取的图像特征.
- 使用人工神经网络 (ANN) 将LMO特征映射到可见度值.
主要成果:
- 自动化的次区域选择尽量减少了对ANN培训的冗余信息.
- 与单图像方法相比,在可见度估计中获得了更高的准确性.
- 证明了地标对象提取用于目标特征生成的有效性.
结论:
- 拟议的方法为可见度估计提供了更准确和自动化的方法.
- 标志性物体提取是一种可行的技术,用于识别环境分析的关键图像区域.
- 这一进步对加强环境监测和空气质量评估有影响.
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