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相关概念视频

Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Aggregates Classification01:29

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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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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

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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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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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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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多尺度特征学习,使用统一模型对高光谱图像分类的统一模型.

Tahir Arshad1, Junping Zhang1, Inam Ullah2

  • 1School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
概括

这项研究介绍了一种超光谱图像分类的新型统一模型,集成Swin变压器,CNN和编码解码器,用于高级多尺度特征学习. 该模型在基准数据集上实现了更高的分类准确性.

关键词:
卷积神经网络是一种卷积神经网络.深度学习模型的深度学习模型功能提取 特性提取超光谱图像分类的分类方法多尺度的特征是多个尺度的特征.斯温变压器 的变压器

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科学领域:

  • 计算机科学 计算机科学
  • 遥感 遥感 遥感 遥感
  • 人工智能的人工智能

背景情况:

  • 超光谱图像分类需要复杂的特征提取,以获得高精度.
  • 现有的方法往往难以有效地捕捉全球和本地光谱空间特征.
  • 多尺度特征学习对于解释复杂的超频谱数据至关重要.

研究的目的:

  • 为高光谱图像分类提出一个先进的建筑范式.
  • 开发一个统一的模型,协同结合Swin变压器,卷积神经网络 (CNN) 和编码器解码器分支.
  • 增强多级特征学习,以提高分类准确度.

主要方法:

  • 一个统一的模型架构整合了三个专门的分支:Swin变压器用于远程依赖,CNN用于本地化功能,以及编码解码器用于全面分析.
  • 多级特征学习利用每个分支的独特能力来捕捉光谱和空间的复杂性.
  • 对公开可用的高光谱数据集 (徐州,萨利纳斯,LK) 的实验评估和与最先进的方法进行比较.

主要成果:

  • 与现有的最先进的方法相比,拟议的统一模型实现了优越的分类性能.
  • 在徐州数据集上,总体准确度为96.87%,在萨利纳斯数据集上为98.48%,在LK数据集上为98.62%.
  • 通过协同的分支集成,证明了多尺度光谱和空间信息的有效同化.

结论:

  • 拟议的统一模型为高光谱图像分类提供了一个强大的方法.
  • 斯温变压器,CNN和编码解码器的协同集成有效地促进了多级特征学习.
  • 该模型在基准数据集上的高精度验证了其有效性和对现实应用的潜力.