遥感变压器:一个系统的审查和分析
Ruikun Wang1,2, Lei Ma3, Guangjun He1,2
1Beijing Institute of Satellite Information Engineering, Beijing 100095, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
遥感 (RS) 的变压器在土地使用分类和融合方面表现出高精度. 需要进一步的研究来提高它们的参数效率和推断速度,以便在更广泛的应用中使用.
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:
- 自2021年以来,变压器模型在遥感 (RS) 中的研究增加了.
- 目前还没有对RS中的变压器应用进行审查.
研究的目的:
- 在过去两年里,对RS中变压器的主要研究趋势进行定量分析.
- 确定RS中变压器的关键应用领域和性能特征.
主要方法:
- 对RS的变压器研究进行了定量分析.
- 应用程序被分为八个领域:LULC分类,细分,融合,变更检测,对象检测,对象识别,注册等.
主要成果:
- 变压器在土地使用/土地覆盖 (LULC) 分类和数据融合方面表现出更高的准确性.
- 在细分和对象检测任务中观察到稳定的性能.
- 变压器比卷积神经网络 (CNN) 需要更多的参数,需要提高推理速度.
- 常见的应用场景包括城市,农田和水体,主要是农业和环境保护等自然科学领域.
结论:
- 变压器在各种RS应用中提供了巨大的潜力,特别是在分类和融合方面.
- 解决参数数量和推断速度对于提高RS中的变压器性能至关重要.
- 未来的研究应该专注于优化RS数据的变压器架构,并扩大其应用范围.
关键词:
变化检测检测的变化检测这是分类分类的分类.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.图像融合 图像融合 图像融合对象检测检测对象检测对象检测经常性神经网络 (RNN)细分化 细分化的细分化时间序列时间序列更多相关视频
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