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Bandpass Sampling01:17

Bandpass Sampling

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
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Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview01:02

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

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Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material,  molecules absorb light depending on the energy required for...
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相关实验视频

Updated: Jan 16, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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使用光谱空间代贪算法进行无监督的高光谱带选择.

Xin Yang1, Wenhong Wang1

  • 1College of Computer Science, Liaocheng University, Liaocheng 252000, China.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
概括
此摘要是机器生成的。

超光谱带选择 (BS) 通过新的光谱空间代贪算法 (SSIGA) 得到了改进. 该方法有效地使用空间和光谱信息,以更好地减少超光谱遥感图像 (HSI) 中的数据维度.

关键词:
频段的选择 频段的选择超光谱遥感图像的使用代的贪算法 代的贪算法当地搜索本地搜索频谱空间信息 频谱空间信息

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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
  • 数据科学数据科学数据科学

背景情况:

  • 超光谱带选择 (BS) 对于降低超光谱遥感图像 (HSI) 中的数据维度至关重要.
  • 现有的基于搜索的BS方法往往无法充分利用固有的空间和光谱信息,从而限制了它们的有效性.
  • 需要无监督的BS方法来整合空间和光谱的先前信息.

研究的目的:

  • 提出一种新的无监督频段选择方法,即光谱空间代贪算法 (SSIGA).
  • 解决现有方法的局限性,有效地利用高质量信息系统中的空间和光谱先前信息.
  • 为了提高BS的性能,用于HSI的分类应用.

主要方法:

  • 对于光谱信息处理,SSIGA采用K-means集群与平衡的集群大小约束.
  • 每个集群都构建了一个K-最近邻近图,以促进有效的本地搜索.
  • 一个客观函数通过使用费舍尔分数,超像素细分,信息和相互信息来评估带的可区分性和冗余性.

主要成果:

  • 与最先进的方法相比,SSIGA在三个真实HSI数据集上表现出优越的性能.
  • 该算法有效地利用空间和光谱信息进行频段选择.
  • 实验结果验证了拟议的目标功能和本地搜索策略的有效性.

结论:

  • 拟议的SSIGA是HSI有效的无监督频段选择方法.
  • 通过整合光谱和空间信息,SSIGA实现了卓越的性能.
  • 该方法为高光谱图像分类中的维度减小提供了有希望的方法.