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

Classification of Systems-I01:26

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:
Classification of Signals01:30

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-II01:31

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,
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

You might also read

Related Articles

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

Sort by
Same author

Biologger-based monitoring of body temperature, heart rate, and heart rate variability in Lidia cattle: relationships with environmental conditions.

International journal of biometeorology·2026
Same author

Prenatal Deep Phenotyping in Genetic Syndromes Diagnosed in the First Trimester of Pregnancy.

Prenatal diagnosis·2026
Same author

Behavioural study of rams subjected to photoperiod change: sexual, social, vital and group activities monitored by video.

Animal reproduction science·2025
Same author

Model for the prediction of therapeutic failures of the 52 mg levonorgestrel intrauterine device.

European journal of obstetrics, gynecology, and reproductive biology·2025
Same author

Effect of melatonin implants on carcass characteristics and meat quality of slow-growing chickens.

Poultry science·2025
Same author

Typification and Characterization of Different Livestock Production Systems of Mediterranean Dairy Sheep Farms with Different Degrees of Intensification: A Comparative Study.

Animals : an open access journal from MDPI·2025

Related Experiment Video

Updated: May 24, 2026

Multimodal Optical Imaging Platform for Studying Cellular Metabolism
04:47

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Published on: June 6, 2025

Multi-channel morphological profiles for classification of hyperspectral images using support vector machines.

Javier Plaza1, Antonio J Plaza, Cristina Barra

  • 1Department of Technology of Computers and Communications, University of Extremadura / Escuela Politécnica de Cáceres, Avenida de la Universidad s/n, E-10071 Cáceres, Spain; E-Mails: jplaza@unex.es ; crbaar@unex.es.

Sensors (Basel, Switzerland)
|March 6, 2012
PubMed
Summary

This study introduces multi-channel morphological profiles for hyperspectral image classification. These profiles enhance feature extraction, improving classification accuracy with limited training data for remote sensing applications.

Keywords:
Hyperspectral imagingland-cover classificationmorphological profilesremote sensingspatial-spectral classificationsupport vector machine (SVM)vector ordering

More Related Videos

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

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

Published on: June 18, 2021

Related Experiment Videos

Last Updated: May 24, 2026

Multimodal Optical Imaging Platform for Studying Cellular Metabolism
04:47

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Published on: June 6, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

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

Published on: June 18, 2021

Area of Science:

  • Remote Sensing
  • Image Processing
  • Machine Learning

Background:

  • Hyperspectral imaging provides rich spectral information but faces challenges in supervised classification due to high dimensionality and limited training samples.
  • Traditional feature extraction methods struggle with the complexity of hyperspectral data, impacting classification performance.
  • Support Vector Machines (SVMs) are powerful classifiers but require effective feature representation for optimal results.

Purpose of the Study:

  • To explore the efficacy of multi-channel morphological profiles for feature extraction in hyperspectral image classification.
  • To investigate various vector ordering strategies for multi-channel morphological transformations.
  • To propose a reduced implementation of multi-channel morphological profiles using dimensionality reduction.

Main Methods:

  • Utilized multi-channel morphological profiles for feature extraction from hyperspectral data.
  • Investigated several vector ordering strategies to adapt morphological transformations to multidimensional pixel vectors.
  • Implemented a reduced version of the multi-channel morphological profile using principal components analysis (PCA).
  • Applied Support Vector Machines (SVMs) for supervised classification of hyperspectral datasets.

Main Results:

  • Multi-channel morphological profiles significantly improved feature extraction compared to single-channel profiles.
  • The proposed reduced implementation offered an efficient alternative for building comprehensive feature sets.
  • Experimental results demonstrated enhanced classification performance, particularly with small training sets.
  • Validated findings using hyperspectral data from NASA's AVIRIS and Germany's DAIS 7915 sensors.

Conclusions:

  • Multi-channel morphological profiles are effective for enhancing feature extraction in hyperspectral remote sensing.
  • The method shows promise for improving supervised classification accuracy, especially when training data is scarce.
  • The study contributes a valuable technique for leveraging the spectral information in hyperspectral imagery for classification.