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

Ranks01:02

Ranks

505
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
505
State Space Representation01:27

State Space Representation

573
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
573
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.5K
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
1.5K
Control Volume and System Representations01:16

Control Volume and System Representations

1.6K
Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
1.6K
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

208
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
208
Wilcoxon Rank-Sum Test01:21

Wilcoxon Rank-Sum Test

755
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
755

You might also read

Related Articles

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

Sort by
Same author

Analysis of the movement of permanent GNSS stations in Spain with directional statistics.

Scientific reports·2026
Same author

Domain-Adaptive Mamba for Cross-Scene Hyperspectral Image Classification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Masked Self-Attention Fusion Network for Joint Classification of Hyperspectral and LiDAR Data.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Self-Supervised Masked Graph Autoencoder for Hyperspectral Anomaly Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

Multi-Scale Autoencoder Suppression Strategy for Hyperspectral Image Anomaly Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

SRViT: Self-Supervised Relation-Aware Vision Transformer for Hyperspectral Unmixing.

IEEE transactions on neural networks and learning systems·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

A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral

Yi Zhang1, Zebin Wu2,3, Jin Sun4

  • 1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. yzhang@njust.edu.cn.

Sensors (Basel, Switzerland)
|October 28, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a distributed parallel algorithm (DPA) to speed up low-rank and sparse representation (LRASR) for hyperspectral image analysis. The DPA significantly accelerates anomaly detection in large datasets while maintaining accuracy.

Keywords:
anomaly detectionapache sparkcloudsdistributed and parallel computinghyperspectral images

More Related Videos

Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis
08:22

Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis

Published on: October 27, 2020

4.3K
Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.9K

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
Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis
08:22

Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis

Published on: October 27, 2020

4.3K
Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.9K

Area of Science:

  • Remote Sensing
  • Computer Science
  • Data Science

Background:

  • Anomaly detection is crucial for hyperspectral image processing.
  • Low-rank and sparse representation (LRASR) methods offer accurate anomaly detection.
  • Large hyperspectral image datasets pose computational challenges for existing LRASR techniques.

Purpose of the Study:

  • To develop a novel distributed parallel algorithm (DPA) for accelerating LRASR.
  • To enhance the processing speed of hyperspectral anomaly detection on cloud computing architectures.
  • To address the computational demands of analyzing large-scale hyperspectral image repositories.

Main Methods:

  • Redesigned LRASR key operators using the MapReduce model for parallel execution on Spark.
  • Developed an optimized data format, storage strategy, and pre-merge mechanism for efficient DPA processing.
  • Implemented a repartitioning policy to further improve algorithm efficiency.

Main Results:

  • The DPA achieved significant speedups in accelerating LRASR for hyperspectral anomaly detection.
  • Maintained comparable accuracy to existing LRASR methods.
  • Demonstrated scalability with an increasing number of computing nodes.

Conclusions:

  • The proposed DPA effectively accelerates LRASR on cloud platforms for hyperspectral image analysis.
  • The algorithm is capable of processing big hyperspectral data efficiently.
  • This approach offers a scalable solution for large-scale remote sensing data processing.