Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Force Classification01:22

Force Classification

1.1K
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,...
1.1K
Classification of Signals01:30

Classification of Signals

371
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...
371
Classification of Systems-I01:26

Classification of Systems-I

167
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:
167
Classification of Systems-II01:31

Classification of Systems-II

133
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,
133
Convolution Properties II01:17

Convolution Properties II

163
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
163
Deconvolution01:20

Deconvolution

127
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
127

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

[Scapular belt for the treatment of comminuted fractures of scapula].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2010
Same author

Manipulation of ordered nanostructures of protonated polyoxometalate through covalently bonded modification.

Chemistry (Weinheim an der Bergstrasse, Germany)·2010
Same author

Developments in nonsteroidal antiandrogens targeting the androgen receptor.

ChemMedChem·2010
Same author

Dynamic presentation of immobilized ligands regulated through biomolecular recognition.

Journal of the American Chemical Society·2010
Same author

[Research on crop-weed discrimination using a field imaging spectrometer].

Guang pu xue yu guang pu fen xi = Guang pu·2010
Same author

A palladium/copper bimetallic catalytic system: dramatic improvement for Suzuki-Miyaura-type direct C-H arylation of azoles with arylboronic acids.

Chemistry (Weinheim an der Bergstrasse, Germany)·2010
Same journal

Application of ephrin-B2 loaded glycol chitosan-silk fibroin hydrogel in the treatment of diabetic refractory wounds.

Scientific reports·2026
Same journal

International expert Delphi consensus on thromboprophylaxis in metabolic and bariatric surgery.

Scientific reports·2026
Same journal

Assessing the cross-region knowledge transfer capability of selected deep learning building vectorization methods in the context of available training datasets.

Scientific reports·2026
Same journal

Feasibility and preliminary effects of outdoor versus indoor cognitive-motor therapy in women with Alzheimer's disease: A randomized single-blind pilot study.

Scientific reports·2026
Same journal

Hallmarks of social action in the vocal turn-taking of wild common marmosets (Callithrix jacchus).

Scientific reports·2026
Same journal

Role and mechanism of AOPPs-induced NOX4-mediated ferroptosis in intervertebral disc degeneration.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Jun 23, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

使用新型卷积网络STConvNeXt进行高效的遥感图像分类.

Bo Liu1, Chenmei Zhan1, Cheng Guo1

  • 1Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China.

Scientific reports
|March 12, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了STConvNeXt,这是用于遥感图像分类的轻量级网络. 它显著减少参数和计算,同时提高准确性,为复杂的图像数据提供有效的解决方案.

关键词:
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.遥感是一种远程传感.SMConvvv 的意思树木结构 树木结构

更多相关视频

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

相关实验视频

Last Updated: Jun 23, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

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:

  • 遥感图像分类面临着复杂的空间结构,高度的类间相似性和类内变异性所带来的挑战.
  • 现有的方法往往难以平衡计算效率与有效的特征提取.

研究的目的:

  • 提出一个创新的轻型卷积网络,STConvNeXt,用于高效和准确的遥感场景分类.
  • 为了提高特征提取和分类性能,同时最大限度地减少计算资源.

主要方法:

  • 开发了STConvNeXt,具有基于分割的移动卷积模块,具有层次树结构.
  • 整合了参数化的深度可分离卷积和快速金字塔聚合模块,以实现高效的特征融合和上下文意识.
  • 引入了一个动态值损失函数,具有可学习的跨类边际,以改善对具有挑战性的样本的歧视.

主要成果:

  • 与ConvNeXt基线相比,STConvNeXt减少了56.49%的参数数量和49.89%的FLOP.
  • 与基线相比,实现了1.2-2.7%的更好的分类准确度.
  • 与最先进的遥感场景分类模型相比,表现出卓越的性能.

结论:

  • STConvNeXt为远程传感图像分类提供了一种计算效率高且高效的解决方案.
  • 拟议的架构模块和培训策略显著提高了分类性能.
  • 尽管参数和计算负载大幅减少,但该模型仍然保持了卓越的准确性.