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MPGH-FS:一个混合特征选择框架,用于强大的多时代OBIA分类.

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  • 1College of Geological and Surveying Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

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此摘要是机器生成的。

这项研究引入了一种用于高分辨率遥感图像的新型特征选择框架 (MPGH-FS). MPGH-FS显著减少了特征尺寸,提高了分类准确性和时间适应性.

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在这里,我们将会看到一个很棒的游戏.在HC中,HC是指HC.在MICC中,MICC是最重要的.欧比亚 (OBIA) 是一个对象.功能选择 功能选择

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科学领域:

  • 遥感 遥感 遥感 遥感
  • 图像分析 图像分析
  • 计算机科学 计算机科学

背景情况:

  • 高分辨率遥感中的基于对象的图像分析 (OBIA) 由于高维特征而经常遭受维度的诅咒.
  • 这导致分类效率和通用性降低.
  • 现有的单一标准特征选择方法在时间适应性方面存在局限性.

研究的目的:

  • 提出一个新的特征选择框架,MPGH-FS,解决维度的诅咒,提高远程传感图像分类的时间适应性.
  • 整合相互信息相关系数 (MICC) 预先过,基因算法 (GA) 全球搜索和登 (HC) 局部优化,以进行强大的特征选择.
  • 在特征压缩,分类准确性和跨时代可转移性方面评估框架的性能.

主要方法:

  • 开发了相互信息预过和遗传登混合特征选择 (MPGH-FS) 框架.
  • 集成的MICC预先过用于初始特征选.
  • 用于全球搜索的GA和用于特征子集本地优化的HC.
  • 使用多时间GF-2卫星图像 (2018-2023) 进行实验.

主要成果:

  • MPGH-FS将特征尺寸从232缩小到9. MPGH-FS将特征尺寸从232缩小到9. MPGH-FS将特征尺寸从232缩小到9. MPGH-FS将特征尺寸从232缩小到9.
  • 在全场景分类中实现了高整体精度 (OA) 85.55%和0.75的卡帕系数.
  • 证明了有效的训练 (6秒) 和推断 (1分钟) 时间.
  • 在跨时间转移实验中,表现出对年间变化的稳定性,分类准确度波动低于4%.

结论:

  • MPGH-FS有效地压缩功能,增强分类性能,并在多时远程传感中提高时间适应性.
  • 拟议的框架为高效和准确的遥感图像分类提供了一个强大的解决方案.
  • MPGH-FS克服了单一标准选择的局限性,并提高了在不同时间段的概括性.