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

Density

Density

Density is an important characteristic of substances, crucial in determining whether an object sinks or floats in a fluid. Its SI unit is kg/m3, and its cgs unit is g/cm3. The density of an object helps in identifying its composition, and also reveals information about the phase of the matter and its substructure. The densities of liquids and solids are roughly comparable, consistent with the fact that their atoms are in close contact. However, gases have much lower densities than liquids and...

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...

State Space Representation

State Space Representation

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...

You might also read

Related Articles

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

Sort by

Same author

Molecular Recognition Mechanism of Key VOCs by Odorant-Binding Proteins in the Western Corn Rootworm (<i>Diabrotica virgifera virgifera</i>).

Insects·2026

Same author

Microtubule-associated CCDC112 is essential for spermiogenesis and male fertility in mice.

Journal of molecular cell biology·2026

Same author

RGS22 is a metazoa-specific radial spoke component required for coordinated ciliary beating.

Nature communications·2026

Same author

USP21-mediated deubiquitylation stimulates NuMA recruitment to the cell cortex to promote mitotic spindle orientation.

Cell death and differentiation·2026

Same author

Characterization of membrane structures regulating primary ciliogenesis by quantitative isotropic ultrastructure imaging.

Nature communications·2026

Same author

Determination of the 90% effective dose of propofol combined with oliceridine or fentanyl for inhibiting the insertion response in upper gastrointestinal endoscopy: a dose-finding trial.

Frontiers in medicine·2026

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 31, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral

Na Li¹, Ruihao Wang¹, Huijie Zhao¹

¹School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)

|January 1, 2020

Summary

This summary is machine-generated.

A new hyperspectral image classification method tackles the small sample size problem using diverse density and sparse representation. This approach enhances spectral feature accuracy, achieving high classification performance on real-world data.

Keywords:

diverse density hyperspectral image classification small sample size sparse representation

More Related Videos

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

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

Related Experiment Videos

Last Updated: Dec 31, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

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

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

Area of Science:

Remote Sensing
Computer Vision
Signal Processing

Background:

Small sample size (SSS) poses a significant challenge in hyperspectral image classification.
Traditional methods struggle with noisy spectral features and limited training data.

Purpose of the Study:

To propose a novel classification method, NCM_DDSR, addressing the SSS problem in hyperspectral imaging.
To improve the robustness of spectral feature representation against noise interference.

Main Methods:

Utilizes diverse density (DD) model to learn dictionary atoms for spectral feature representation.
Employs an improved matching pursuit (MP) model to obtain sparse coefficients for classification.

Main Results:

Achieved high overall accuracies of 91.59% for PHI data and 92.83% for AVIRIS data.
Obtained high kappa coefficients of 0.897 and 0.91, demonstrating effective classification performance.

Conclusions:

The proposed NCM_DDSR method effectively overcomes the SSS problem in hyperspectral image classification.
The method demonstrates superior performance in handling noisy spectral features and achieving accurate classification.