Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Signals

Classification of Signals

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...

Classification of Systems-II

Classification of Systems-II

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,

Aggregates Classification

Aggregates Classification

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...

Classification of Systems-I

Classification of Systems-I

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:

Thematic Layering in GIS

Thematic Layering in GIS

In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...

Force Classification

Force Classification

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,...

You might also read

Related Articles

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

Sort by

Same author

Association between the Dietary Index for Gut Microbiota and all-cause mortality in coronary heart disease: A retrospective cohort analysis of NHANES (2005-2018).

Medicine·2026

Same author

Dual-Hit Myopia Mechanism Unveiled by Multi-Omics: Opn1mw Deficiency Primed the Retina for Exaggerated Response to Environmental Defocus.

Investigative ophthalmology & visual science·2026

Same author

Emergence of newly reported HIV-1 recombinants CRF120_0107 and CRF149_01B in Guangxi Zhuang Autonomous Region, China: Molecular evidence and public health challenges.

Biosafety and health·2026

Same author

Okra eyelid patch versus sodium hyaluronate combined with ofloxacin eye drop in the treatment of meibomian gland dysfunction: a randomized controlled trial.

BMC ophthalmology·2026

Same author

TRA2A negatively regulates HIV-1-induced macrophage pyroptosis by mediating TXNIP expression in an m6A-dependent manner.

Cell death discovery·2026

Same author

Experimental Evaluation of Reducing Water Cut and Increasing Oil Recovery Using Multiphase Mixed Fluid.

ACS omega·2026

Search research articles

Related Experiment Video

Updated: Dec 4, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Multilayer Spectral-Spatial Graphs for Label Noisy Robust Hyperspectral Image Classification.

Junjun Jiang, Jiayi Ma, Xianming Liu

IEEE Transactions on Neural Networks and Learning Systems

|October 22, 2020

Summary

This summary is machine-generated.

This study introduces a novel method using spectral-spatial graphs (SSGs) to clean noisy labels in hyperspectral image (HSI) classification. The multiscale segmentation-based multilayer SSGs (MSSGs) approach significantly improves classification accuracy with noisy training data.

More Related Videos

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Published on: June 6, 2025

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

Published on: December 1, 2023

Related Experiment Videos

Last Updated: Dec 4, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Published on: June 6, 2025

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

Published on: December 1, 2023

Area of Science:

Remote Sensing
Computer Vision
Machine Learning

Background:

Hyperspectral image (HSI) analysis relies on scarce labeled data, often corrupted by label noise.
Existing supervised HSI classification methods degrade significantly with noisy training samples.

Purpose of the Study:

To propose a robust label noise cleansing method for HSI classification.
To enhance classification accuracy despite the presence of label noise in training data.

Main Methods:

Developed a spectral-spatial graph (SSG) framework for label noise cleansing.
Constructed an affinity graph based on spectral and spatial similarities within superpixel regions.
Introduced multiscale segmentation-based multilayer SSGs (MSSGs) to integrate richer spatial information.

Main Results:

The proposed MSSG method effectively reduces label noise in HSI datasets.
Significantly improved classification accuracy on training data with noisy labels compared to state-of-the-art methods.
Demonstrated superior performance over four major classifiers.

Conclusions:

MSSG offers a powerful solution for handling label noise in HSI classification.
The integration of multiscale spectral-spatial information is crucial for robust HSI analysis.
The method provides a significant advancement for reliable HSI classification in real-world scenarios.