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相关概念视频

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Polymers are classified as linear or branched on the basis of their chain architecture. The polymer chains in linear polymers have a long chain-like structure with minimal to no branching at all. Even if a polymer features large substituent groups on the monomer, which appear as branches to the skeleton, it is not considered a branched polymer. A branched polymer contains secondary polymer chains that arise from the main polymer chain. The branching occurs when the polymer growth shifts from...
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According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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在远程传感图像中使用自定义深度CNN架构进行高性能场景分类.

Ahmed M Abdelmonem1, Mohamed Maher Ata2, Abdelhamied A Atey3

  • 1Department of Electronics and Communications Engineering, Zagazig University, Zagazig, 44519, Egypt.

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概括

一个新的卷积神经网络 (CNN) 架构在遥感图像分类方面表现出色. 这种高效的模型实现了高精度,并结合了可解释性技术,以更好地理解.

关键词:
深度卷积神经网络是一个深度卷积神经网络.多个类的图像图像.认可 承认 认可遥感图像 遥感图像 遥感图像场景的分类 场景的分类

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科学领域:

  • 计算机视觉 计算机视觉
  • 遥感 遥感 遥感 遥感
  • 机器学习 机器学习

背景情况:

  • 准确的多类图像分类对于遥感数据分析至关重要.
  • 现有的卷积神经网络 (CNN) 模型在平衡这些任务的准确性,效率和可解释性方面面临挑战.

研究的目的:

  • 引入一种新,轻量级和高效的CNN架构,用于远程传感中的多类图像分类.
  • 通过使用沙普利增量解释 (SHAP) 和类激活映射 (CAM) 增强模型的解释性.
  • 评估模型在各种遥感数据集中的性能和通用性.

主要方法:

  • 为远程传感图像分类量身定制的混合CNN框架的开发.
  • 对NWPU-RESISC45和UC Merced土地使用数据集的评估.
  • 整合SHAP和CAM以实现模型可解释性.
  • 与五个受欢迎的预训练CNN模型进行比较.

主要成果:

  • 拟议的CNN架构实现了高精度 (0.9428在NWPU-RESISC45,0.93在UC Merced) 和优于现有的模型.
  • 获得了竞争性回忆 (0.94,0.93),精度 (0.95,0.94),IOU (0.89,0.86) 和F1分数 (0.94,0.93). 获得了竞争性回忆 (0.94,0.93),精度 (0.95,0.94),IOU (0.89,0.86) 和F1分数 (0.94,0.93).
  • 证明了高效的训练时间 (NWPU-RESISC45的3692秒,UC Merced的559秒) 与可管理的GPU内存使用.

结论:

  • 新的CNN架构为远程传感图像理解提供了一个引人注目的解决方案,平衡性能,效率和可解释性.
  • 综合可解释性技术 (SHAP,CAM) 提高了模型的可靠性.
  • 该模型在各种数据集中的可通用性证实了它对各种遥感应用的稳定性.