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使用虫优化改进远程传感场景分类,使用增强的深度学习方法优化虫优化.

Mohammad Alamgeer1, Alanoud Al Mazroa2, Saud S Alotaibi3

  • 1Department of Information Systems, Applied College at Mahayil, King Khalid University, Saudi Arabia.

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PubMed
概括
此摘要是机器生成的。

一个新的远程传感场景分类,使用虫优化与增强深度学习 (RSSC-DBOEDL) 方法准确分类卫星图像. 这种方法实现了高精度,改进了现有的土地景观分析技术.

关键词:
深度学习是一种深度学习.泥甲虫优化优化方法遥感图像的远程传感图像.场景的分类 场景的分类转移学习转移学习

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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
  • 人工智能的人工智能

背景情况:

  • 遥感 (RS) 场景分类对于监视,城市规划和环境观察等应用至关重要.
  • 现有的卷积神经网络 (CNN) 方法由于复杂的纹理,杂乱的场景和尺度变化而与RSI作斗争.
  • 从遥感图像 (RSI) 准确地分类土地场景仍然是一个挑战.

研究的目的:

  • 开发一种先进的远程传感场景分类,使用虫优化与增强深度学习 (RSSC-DBOEDL) 方法.
  • 在远程传感图像 (RSI) 中有效分类各种场景.
  • 为了提高RSI分类的准确性和上下文理解.

主要方法:

  • 利用一个增强的MobileNet模型作为RSI的主要特征提取器.
  • 用户使用了泥虫优化 (DBO) 来对增强的MobileNet.net进行超参数调整.
  • 实施了基于注意力的多头长期短期记忆 (MHA-LSTM) 网络,用于场景分类.

主要成果:

  • 在UC Merced数据集上,RSSC-DBOEDL方法实现了98.75%的高准确率.
  • 该方法在EuroSAT数据集上表现出强的性能,准确度为95.07%.
  • 在基准RSI数据集中分类场景的现有方法中表现优于现有方法.

结论:

  • RSSC-DBOEDL方法为遥感场景分类提供了一种优越的方法.
  • 将DBO和MHA-LSTM与增强的移动网络集成,显著提高了分类准确性.
  • 这种技术为分析复杂的遥感图像提供了强大的解决方案.