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 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 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...
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...
Force Classification01:22

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,...
Aggregates Classification01:29

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...

You might also read

Related Articles

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

Sort by
Same author

Anomaly detection in smart power grids with graph-regularized MS-SVDD: a multimodal subspace learning approach.

Scientific reports·2026
Same author

Perception-Inspired Network for Stereo Image Quality Assessment.

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

A multimodal drowsiness dataset using video, biometric, and behavioral data.

Scientific data·2026
Same author

A multimodal stress detection dataset with facial expressions and physiological signals.

Scientific data·2025
Same author

Crucial-Diff: A Unified Diffusion Model for Crucial Image and Annotation Synthesis in Data-Scarce Scenarios.

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

Optimizing the Output of Long Short-Term Memory Cell for High-Frequency Forecasting in Financial Markets.

IEEE transactions on neural networks and learning systems·2025

Related Experiment Videos

Collective Network of Binary Classifier Framework for Polarimetric SAR Image Classification: An Evolutionary

Serkan Kiranyaz, Turker Ince, Stefan Uhlmann

    IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
    |April 7, 2012
    PubMed
    Summary

    A new Collective Network of Binary Classifiers (CNBC) framework enhances terrain classification in polarimetric synthetic aperture radar (SAR) images. This evolutionary approach optimizes feature selection, combination, and classifier configuration for superior accuracy.

    Related Experiment Videos

    Area of Science:

    • Remote Sensing
    • Machine Learning
    • Image Analysis

    Background:

    • Terrain classification using polarimetric synthetic aperture radar (SAR) images is a challenging research area.
    • Existing methods face challenges in optimal feature selection, combination, distance metric application, classifier configuration, scalability, and efficient training.

    Purpose of the Study:

    • To introduce a novel Collective Network of Binary Classifiers (CNBC) framework.
    • To address key unanswered questions in SAR terrain classification for improved performance.

    Main Methods:

    • The CNBC framework employs a 'Divide and Conquer' strategy.
    • It utilizes evolutionary search to identify optimal binary classifiers (BCs) for each class.
    • The framework allows dynamic adaptation to new data without full retraining.

    Main Results:

    • The proposed CNBC framework demonstrates superior performance in terrain classification.
    • Evaluations on benchmark SAR images show a significant performance gap compared to existing classifiers.
    • The framework effectively handles feature selection, combination, and classifier optimization.

    Conclusions:

    • The CNBC framework offers a robust and adaptive solution for SAR terrain classification.
    • It achieves high classification accuracy by optimizing multiple aspects of the classification process.
    • This approach significantly advances the field of polarimetric SAR image analysis.