Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.0K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.0K
Detection of Black Holes01:10

Detection of Black Holes

2.6K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.6K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.3K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.3K
Effects of EDTA on End-Point Detection Methods01:18

Effects of EDTA on End-Point Detection Methods

664
Different methods, such as visual observance of metal-ion indicators, spectroscopic techniques, and potentiometric methods, can determine the endpoint of an EDTA titration.
In the visual method, metal-ion indicators (metallochromic dyes), which have distinct colors in their free and complex forms, are added to the mixture to signal the titration's end point. They form stable complexes with metal ions, but these complexes are weaker than the corresponding metal–EDTA complexes. As a...
664
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

11.4K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
11.4K
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

6.0K
In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...
6.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Complete Telomere-To-Telomere Assembly of <i>Plectropomus leopardus</i> and Phylogenomic Insights Into Perciformes.

Evolutionary applications·2026
Same author

Corrigendum to "Deciphering teleost testis development throughout the annual cycle: Insights from transcriptomic features and thyroid hormone signaling pathway" [Theriogenology 260 (2026) 117932].

Theriogenology·2026
Same author

Comparative Transcriptomics and Metabolomics Analysis Revealed the Mechanism of Exogenous Salicylic Acid Improving the Cold Tolerance of Walnut.

International journal of molecular sciences·2026
Same author

Deciphering teleost testis development throughout the annual cycle: Insights from transcriptomic features and thyroid hormone signaling pathway.

Theriogenology·2026
Same author

Chromosome-level genome assembly of the coral grouper, Epinephelus corallicola and its evolutionary insights into Eupercaria.

BMC genomics·2025
Same author

The chromosome-level genome assembly and annotation of the silver-lipped pearl oyster, Pinctada maxima.

Scientific data·2025

Related Experiment Video

Updated: Feb 3, 2026

Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals
07:24

Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals

Published on: April 14, 2020

18.6K

Hierarchical Sub-Pixel Anomaly Detection Framework for Hyperspectral Imagery.

Wenzheng Wang1,2, Baojun Zhao3,4, Fan Feng5,6

  • 1School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China. wwz@bit.edu.cn.

Sensors (Basel, Switzerland)
|October 31, 2018
PubMed
Summary
This summary is machine-generated.

A new hierarchical Reed-Xiaoli (H-RX) method improves hyperspectral anomaly detection, especially for small sub-pixel targets. This framework enhances detection accuracy by iteratively refining results and incorporating spatial information.

Keywords:
RXanomaly detectionhierarchical structurehyperspectral image (HSI) analysis

More Related Videos

Three-Dimensional Printing of a Complex Aortic Anomaly
03:40

Three-Dimensional Printing of a Complex Aortic Anomaly

Published on: November 1, 2018

7.1K
Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
11:19

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

10.9K

Related Experiment Videos

Last Updated: Feb 3, 2026

Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals
07:24

Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals

Published on: April 14, 2020

18.6K
Three-Dimensional Printing of a Complex Aortic Anomaly
03:40

Three-Dimensional Printing of a Complex Aortic Anomaly

Published on: November 1, 2018

7.1K
Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
11:19

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

10.9K

Area of Science:

  • Remote Sensing
  • Signal Processing
  • Data Analysis

Background:

  • Anomaly detection is crucial in hyperspectral imaging.
  • Traditional Reed-Xiaoli (RX) methods struggle with sub-pixel targets due to spectral similarity with background.
  • Sub-pixel anomalies often lead to false alarms in standard RX anomaly detection.

Purpose of the Study:

  • To introduce a novel Hierarchical Reed-Xiaoli (H-RX) anomaly detection framework.
  • To enhance the detection of small, sub-pixel anomalies in hyperspectral data.
  • To improve the overall performance and reduce false alarms compared to traditional methods.

Main Methods:

  • The proposed H-RX method utilizes multiple layers of the original RX anomaly detector.
  • Each layer applies a nonlinear function to restrain RX output, influencing subsequent iterations.
  • A spatial regularization layer is incorporated to boost sub-pixel anomaly detection capabilities.

Main Results:

  • Extensive experiments were conducted on three hyperspectral image datasets.
  • The H-RX algorithm demonstrated superior performance over the standard RX algorithm.
  • The proposed method also outperformed other classical anomaly detection techniques.

Conclusions:

  • The hierarchical framework effectively addresses limitations of traditional RX methods for sub-pixel anomalies.
  • H-RX offers enhanced accuracy and robustness in hyperspectral anomaly detection.
  • The spatial regularization component is key to improving sub-pixel target identification.