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

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Methods of Obtaining Topography01:25

Methods of Obtaining Topography

Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
The Evidence for Evolution02:55

The Evidence for Evolution

Genetic variations accumulating within populations over generations give rise to biological evolution. Evolutionary changes can result in the formation of novel varieties and entire new species. These changes are responsible for the diverse forms of life inhabiting the planet. The evidence for evolution suggests that all living organisms descended from common ancestors.

You might also read

Related Articles

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

Sort by
Same author

Discordance Between Systemic Lupus Erythematosus Disease Activity Index Domain Weights and Their Association With Organ Damage Accrual.

Arthritis care & research·2026
Same author

Surgical data science - from concepts toward clinical translation.

Medical image analysis·2021
Same author

An automatic framework for fusing information from differently stained consecutive digital whole slide images: A case study in renal histology.

Computer methods and programs in biomedicine·2021
Same author

Time series extrinsic regression: Predicting numeric values from time series data.

Data mining and knowledge discovery·2021
Same author

Graph-based description of tertiary lymphoid organs at single-cell level.

PLoS computational biology·2020
Same author

Deconstructing the diagnostic reasoning of human versus artificial intelligence.

CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne·2019
Same journal

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.

International journal of neural systems·2026
Same journal

Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images.

International journal of neural systems·2026
Same journal

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

International journal of neural systems·2026
Same journal

Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer.

International journal of neural systems·2026
Same journal

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals.

International journal of neural systems·2026
Same journal

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

International journal of neural systems·2026
See all related articles

Related Experiment Video

Updated: May 27, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

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

Published on: October 11, 2016

Discovering significant evolution patterns from satellite image time series.

François Petitjean1, Florent Masseglia, Pierre Gançarski

  • 1LSIIT-UMR 7005, Pôle API, Bd Sébastien Brant-BP 10413, 67412 Illkirch Cedex, France. fpetitjean@unistra.fr

International Journal of Neural Systems
|December 2, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a framework for analyzing satellite image time series (SITS) to uncover land cover changes. The method effectively extracts frequent sequential patterns, revealing land cover evolution over time.

More Related Videos

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
10:28

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Published on: June 13, 2020

Related Experiment Videos

Last Updated: May 27, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

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

Published on: October 11, 2016

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
10:28

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Published on: June 13, 2020

Area of Science:

  • Earth Observation
  • Data Mining
  • Geospatial Analysis

Background:

  • Satellite Image Time Series (SITS) are crucial for understanding land cover dynamics.
  • Analyzing SITS is complex due to varying change durations and multi-dimensional pixel data.
  • Non-evolving regions often obscure significant land cover change patterns.

Purpose of the Study:

  • To develop a framework for mining frequent sequential patterns in SITS.
  • To address challenges posed by multi-dimensional data and overwhelming non-evolving regions.
  • To enable the discovery of land cover evolution behaviors from satellite imagery.

Main Methods:

  • Utilizing frequent sequential pattern mining (FSPM) principles adapted for SITS.
  • Developing specific algorithms to handle multi-dimensional radiometric data.
  • Implementing a framework to overcome data complexity and identify subtle changes.

Main Results:

  • The proposed framework successfully extracts relevant land cover evolution behaviors.
  • Demonstrated ability to identify both long-term and short-term changes.
  • Experimental validation on 35 images over 20 years confirms the approach's efficacy.

Conclusions:

  • The developed SITS mining framework effectively discovers land cover evolution patterns.
  • The approach provides a valuable tool for analyzing long-term geospatial changes.
  • This method enhances our understanding of dynamic environmental processes captured by satellite data.