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

335
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:
335
Classification of Systems-II01:31

Classification of Systems-II

245
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,
245
Classification of Signals01:30

Classification of Signals

947
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...
947
Aggregates Classification01:29

Aggregates Classification

395
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...
395

You might also read

Related Articles

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

Sort by
Same author

Retraction Note: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda.

Journal of ambient intelligence and humanized computing·2026
Same author

Bald eagle-optimized transformer networks with temporal-spatial mid-level features for pancreatic tumor classification.

Biomedical physics & engineering express·2025
Same author

A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification.

Scientific reports·2025
Same author

A multi-patch-based deep learning model with VGG19 for breast cancer classifications in the pathology images.

Digital health·2025
Same author

XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer.

Scientific reports·2024
Same author

Optimizing pulmonary chest x-ray classification with stacked feature ensemble and swin transformer integration.

Biomedical physics & engineering express·2024

Related Experiment Video

Updated: Sep 24, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

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

Published on: February 9, 2024

1.6K

Computational Intelligence for Observation and Monitoring: A Case Study of Imbalanced Hyperspectral Image Data

Debaleena Datta1, Pradeep Kumar Mallick1, Jana Shafi2

  • 1School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar 751024, India.

Computational Intelligence and Neuroscience
|May 10, 2022
PubMed
Summary

Hyperspectral image imbalance is addressed using resampling techniques like SMOTE and Tomek Links. Ensemble classifiers, including random rotation forest, achieved higher accuracy on benchmark datasets.

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

2.5K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.2K

Related Experiment Videos

Last Updated: Sep 24, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

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

Published on: February 9, 2024

1.6K
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

2.5K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.2K

Area of Science:

  • Remote Sensing
  • Computer Vision
  • Data Science

Background:

  • Hyperspectral image analysis faces challenges due to data imbalance, hindering classification accuracy.
  • Existing resampling techniques for imbalance mitigation in hyperspectral data are limited.
  • Novel illustrative study needed to evaluate diverse resampling strategies.

Purpose of the Study:

  • To investigate the performance of oversampling, undersampling, and hybrid sampling techniques for hyperspectral data imbalance.
  • To classify balanced hyperspectral datasets using spectral and spatial features with tree-based ensemble classifiers.
  • To introduce and evaluate a novel ensemble hybrid classifier, random rotation forest.

Main Methods:

  • Applied oversampling, undersampling, and hybrid sampling (SMOTE, Tomek Links) to hyperspectral datasets.
  • Utilized spectral and spatial features for classification with ensemble methods.
  • Evaluated performance using precision, recall, F-score, Cohen kappa, and overall accuracy.
  • Tested on Indian Pines, Salinas Valley, and Pavia University datasets.

Main Results:

  • SMOTE, Tomek Links, and their combinations proved to be optimized resampling strategies.
  • Ensemble classifiers, particularly rotation forest and random rotation ensemble, demonstrated superior accuracy.
  • Comparative statistical analysis confirmed the effectiveness of selected methods.

Conclusions:

  • Resampling techniques significantly improve hyperspectral data analysis by addressing imbalance.
  • Ensemble classifiers offer enhanced classification accuracy for imbalanced hyperspectral datasets.
  • The random rotation forest classifier shows promise for hyperspectral image classification.