一种线性回归方法,用于在物体到图像空间转换中的最佳扫描线确定,使用推扫描图像
Seyede Shahrzad Ahooei Nezhad1, Mohammad Javad Valadan Zoej1, Fahimeh Youssefi1,2
1Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967-15443, Iran.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
一个新的最佳扫描线确定 (BSD) 框架使用线性回归模型用于光谱应用. 这种方法显著减少了实时处理推扫描图像的计算时间和复杂性.
科学领域:
- 摄影测量和遥感技术的应用
- 地理学工程 工程地质学
- 计算机视觉 计算机视觉
背景情况:
- 线性数组推扫图像在光谱学中将对象转换为图像坐标带来了挑战.
- 现有的最佳扫描线确定 (BSD) 方法依赖于代的对线性方程 (CE) 解决方案,这对于实时应用来说是复杂和耗时的.
- 对于每个扫描线的有效和准确的外部定向参数 (EOP) 确定,这一需求至关重要.
研究的目的:
- 开发一个新的最佳扫描线确定 (BSD) 框架,降低计算复杂度.
- 为了消除在扫描线参数确定中重复使用对准方程 (CE) 的需要.
- 创建一个实用和强大的解决方案,用于实时摄影仪表应用,使用推扫描图像.
主要方法:
- 开发了一个基于线性回归模型 (LRM) 的BSD框架,使用模拟控制点 (SCOP) 进行培训,并使用模拟检查点 (SCP) 进行测试.
- 该方法涉及两阶段的方法:使用SCOP计算LRM参数的训练阶段,以及使用SCP评估准确性和速度的测试阶段.
- 该框架在十个不同的推扫数据集上进行了评估,使用了500万个SCP和有限的一组SCOP.
主要成果:
- 拟议的基于LRM的BSD方法实现了10^-9像素的根平均平方误差 (RMSE),证明了非常高的准确性.
- 与现有的BSS/BSD技术相比,该方法在各种推扫描图像中显著提高了稳定性.
- 执行时间大大缩短,只需要2-3秒,非常适合实时应用.
结论:
- 新型基于LRM的BSD框架提供了一个计算效率高且高度准确的解决方案,用于在推扫描仪图像中确定外部定向参数 (EOP).
- 这种方法克服了传统代方法的局限性,为实时光谱测量任务提供了切实可行的替代方案.
- 该方法的速度,准确性和稳定性使其成为处理线性数组推扫数据的宝贵进步.
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