一个基于功能增强模块和高斯混合模型融合的双分支云检测算法
Fangrong Zhou1, Gang Wen1, Yi Ma1
1Joint Laboratory of Power Remote Sensing Technology (Electric Power Research Institute, Yunnan Power Grid Company ltd.), Kunming 650217, China.
Mathematical biosciences and engineering : MBE
|December 21, 2023
概括
本研究引入了一种新的遥感 (RS) 云检测方法,使用特征增强和高斯混合模型 (GMM). 该方法准确地识别了边缘和破碎的云,提高了整体检测准确度.
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
- 遥感技术 遥感技术 遥感技术
背景情况:
- 准确的云检测对于优化遥感 (RS) 数据利用至关重要.
- 现有的算法很难识别具有挑战性的云类型,如边缘云和破碎云.
研究的目的:
- 开发一种改进的云检测方法,用于远程传感图像.
- 为了提高边缘和破碎云的识别.
主要方法:
- 使用了Himawari-8卫星频道数据.
- 结合了功能增强模块与高斯混合模型 (GMM).
- 采用拉普拉斯运算符来增强云边缘和破碎云的光谱特征.
主要成果:
- 提出的方法显示出与视觉解释有前途的一致性.
- 与随机森林 (RF),K-最近邻居 (KNN) 和标准GMM相比,实现了更高的准确度指标.
- 成功改进了边缘和破碎云的检测.
结论:
- 新型功能增强和GMM组合方法提供高精度云检测功能.
- 这种方法在推进遥感应用方面具有重大潜力.
- 该方法有效地解决了现有的云检测算法的局限性.
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