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联合超像素和变压器用于高分辨率遥感图像分类.

Guangpu Dang1, Zhongan Mao2, Tingyu Zhang3,4

  • 1Shaanxi Provincial Land Engineering Construction Group Land Survey Planning and Design Institute, Xi'an, Shaanxi, China.

Scientific reports
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概括

一个新的联合超像素和变压器 (JST) 框架通过建模对象依赖性来改进高分辨率遥感图像 (HRI) 的分类. 这种方法比传统的多尺度方法提高了准确性.

关键词:
深度学习是一种深度学习.图像的分类图像的分类.遥感图像 遥感图像 遥感图像超级像素是一个超级像素.变压器变压器变压器

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科学领域:

  • 遥感 遥感 遥感 遥感
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 深度神经网络和超像素细分推进了高分辨率遥感图像 (HRI) 的分类.
  • 现有的方法经常在不同尺度上堆叠特征,忽视了对象间的上下文依赖性.

研究的目的:

  • 为HRI分类引入一个联合超像素和变压器 (JST) 框架.
  • 解决当前方法中不考虑细分对象之间的上下文依赖性的局限性.

主要方法:

  • HRI 数据被细分为超像素对象.
  • 使用变压器模型来捕捉超像素对象之间的远程依赖关系.
  • 编码和解码的变压器架构旨在模拟上下文关系并预测对象类.

主要成果:

  • 在两个HRI数据集上,JST框架实现了高分类准确度,整体准确度达到0.91,卡帕系数高达0.89.
  • 该研究探讨了语义范围对分类性能的影响.
  • 定性和定量比较表明,与基准方法相比,JST的竞争性和优异性能.

结论:

  • 拟议的JST框架有效地模拟了对象间的上下文依赖性,以改进HRI分类.
  • JST为分析复杂的遥感图像提供了一种新且有效的方法.
  • 该方法显示了推进HRI分类任务的巨大潜力.