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相关概念视频

Force Classification01:22

Force Classification

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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.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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自主监督的域内表示学习用于遥感图像场景分类.

Ali Ghanbarzadeh1, Hossein Soleimani1

  • 1School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.

Heliyon
|October 9, 2024
PubMed
概括
此摘要是机器生成的。

使用SimSiam对遥感数据的自主监督学习预训可以提高土地覆盖分类的性能. 更高分辨率的数据集为转移学习带来了更具歧视性和可概括性的特征.

关键词:
深度学习是一种深度学习.遥感是一种远程传感.代表性的学习学习.场景的分类 场景的分类自主监督学习学习转移学习转移学习

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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
  • 机器学习 机器学习

背景情况:

  • 来自ImageNet的转移学习显示,由于域差异,遥感的局限性存在.
  • 标注遥感数据是具有挑战性的,而没有标签的数据是丰富的.
  • 自主监督学习 (SSL) 提供了比监督方法更好的表示学习.

研究的目的:

  • 预先训练域内表示用于远程传感图像,使用对比的SSL.
  • 将学到的特征转移到相关的遥感数据集,用于场景分类.
  • 确定影响有效域内特征提取的关键因素.

主要方法:

  • 利用SimSiam算法对遥感数据集进行自我监督的预训.
  • 将预训练重量转移到各种下游场景分类任务中.
  • 进行了线性评估,每类样本数量有限.
  • 分析了使用具有不同属性的数据集进行特征预训练的分析.

主要成果:

  • 在五个土地覆盖分类数据集上取得了最先进的结果.
  • 证明了更高分辨率的预训练数据集会导致更具歧视性和更普遍的表述.
  • 确定了选择最佳数据集以进行域内特征学习的有影响力的因素.

结论:

  • 对比的自我监督学习,特别是SimSiam,对于遥感表示学习是有效的.
  • 在域内预培训显著提高下游任务的性能,特别是有限的标记数据.
  • 数据集分辨率是学习强大和可转移的遥感特征的关键因素.