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

Force Classification01:22

Force Classification

1.2K
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,...
1.2K
Classification of Signals01:30

Classification of Signals

460
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...
460
Aggregates Classification01:29

Aggregates Classification

321
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...
321
Classification of Systems-II01:31

Classification of Systems-II

146
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,
146
Classification of Systems-I01:26

Classification of Systems-I

186
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:
186
Labeling Emotion01:20

Labeling Emotion

139
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
139

You might also read

Related Articles

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

Sort by
Same author

Identification and Genomic Analysis of the First Salmonella typhimurium Monophasic Variant 1,4,[5],12:i:- Isolate Harbouring a Chromosomally Integrated bla<sub>NDM-5</sub>.

Microbial biotechnology·2026
Same author

Development of Hexaploid Wheat Germplasm with Resistance to Both Powdery Mildew and Stripe Rust by Introgression of <i>Pm60</i> and <i>YrU1</i> from <i>Triticum urartu</i>.

Plants (Basel, Switzerland)·2026
Same author

A high-quality genome assembly delineates the genetic basis of freezing tolerance in the elite wheat cultivar Jimai 22.

Journal of genetics and genomics = Yi chuan xue bao·2026
Same author

LaVIDE: Language-Prompted Satellite Change Detection via Map-Image Alignment.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Post-Translational Control of TaFT1 by WAPO1 Ubiquitination Shapes Spike Architecture and Yield in Wheat.

Plant biotechnology journal·2026
Same author

Predictive risk model for bone metastasis in intermediate-risk prostate cancer: a single-center retrospective analysis.

Scientific reports·2026

Related Experiment Video

Updated: Jul 1, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

Satellite Video Multi-Label Scene Classification With Spatial and Temporal Feature Cooperative Encoding: A Benchmark

Weilong Guo, Shengyang Li, Feixiang Chen

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 12, 2024
    PubMed
    Summary

    Researchers developed a new large-scale dataset and a Spatial and Temporal Feature Cooperative Encoding (STFCE) method for satellite video multi-label scene classification. This approach enhances local details and improves classification accuracy for applications like ocean observation and smart cities.

    More Related Videos

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    533
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.7K

    Related Experiment Videos

    Last Updated: Jul 1, 2025

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    533
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.7K

    Area of Science:

    • Earth Observation
    • Computer Vision
    • Machine Learning

    Background:

    • Satellite video multi-label scene classification is crucial for applications like ocean observation and smart cities.
    • Existing datasets lack scale and quality, hindering task improvement.
    • General video methods struggle to capture fine-grained local details in satellite imagery.

    Purpose of the Study:

    • To introduce the first large-scale, publicly available dataset for satellite video multi-label scene classification.
    • To propose a novel baseline method, Spatial and Temporal Feature Cooperative Encoding (STFCE), for enhanced satellite video analysis.
    • To advance the accuracy and robustness of satellite video scene classification.

    Main Methods:

    • Developed a dataset comprising 3549 videos (141960 frames) across 18 classes of ground content.
    • Proposed the STFCE method to exploit spatial-temporal feature relations and model long-term motion.
    • Integrated local detail enhancement and inter-frame variation analysis for robust feature representation.

    Main Results:

    • The STFCE method outperformed 13 state-of-the-art approaches, achieving a Global Average Precision (GAP) of 0.8106.
    • Demonstrated the effectiveness of fusing spatial, temporal, and motion features for improved classification.
    • Benchmarking confirmed the dataset's challenging nature and its potential to drive further research.

    Conclusions:

    • The developed dataset and STFCE method significantly advance satellite video multi-label scene classification.
    • The STFCE model's ability to capture spatial, temporal, and motion information leads to superior performance.
    • The new dataset is expected to foster innovation and development in the field of remote sensing analysis.