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

You might also read

Related Articles

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

Sort by
Same author

Low-Profile Metasurface Antenna for Broadband RCS Reduction and Omnidirectional Radiation.

Materials (Basel, Switzerland)·2026
Same author

UAV-LiDAR high-throughput time-series phenotyping and genome-wide association analysis reveal the genetic basis of plant height in peanut (<i>Arachis hypogaea</i> L.).

Plant phenomics (Washington, D.C.)·2026
Same author

Diving Into Epipolar Transformers for Light Field Super-Resolution and Disparity Estimation.

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

High-resolution imaging of exosome pulmonary spatial distribution via fluorescent mRNA labeling and fMOST.

Biochemical and biophysical research communications·2026
Same author

Small-Target Detection Algorithm Based on Improved YOLOv11n.

Sensors (Basel, Switzerland)·2026
Same author

Resolving the tribal affiliation of the East Asia clade: phylogenomic evidence for its inclusion into Komarovieae (Apiaceae: Apioideae).

Annals of botany·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Mar 17, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.9K

A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution.

Huanxin Zou1, Xianxiang Qin2, Shilin Zhou3

  • 1College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China. hxzou2008@163.com.

Sensors (Basel, Switzerland)
|July 21, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces an improved superpixel algorithm for synthetic aperture radar (SAR) images, enhancing clustering by incorporating likelihood information and a novel edge-evolving scheme for better segmentation accuracy.

Keywords:
edge evolvinggeneralized gamma distributionlikelihoodsimple linear iterative clusteringsuperpixelsynthetic aperture radar

More Related Videos

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

588
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.7K

Related Experiment Videos

Last Updated: Mar 17, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.9K
Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

588
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.7K

Area of Science:

  • Computer Vision
  • Image Processing
  • Remote Sensing

Background:

  • Simple Linear Iterative Clustering (SLIC) is a popular superpixel algorithm.
  • SLIC struggles with Synthetic Aperture Radar (SAR) images due to speckle and intensity variations.
  • Existing methods require improvements for effective SAR image analysis.

Purpose of the Study:

  • To propose an improved SLIC algorithm specifically for SAR images.
  • To enhance superpixel generation by leveraging likelihood information.
  • To address the limitations of traditional SLIC in complex SAR data.

Main Methods:

  • Developed a local clustering scheme combining intensity similarity and spatial proximity.
  • Incorporated generalized gamma distribution (GГD) for estimating cluster likelihood information.
  • Introduced a local edge-evolving scheme for post-processing, integrating spatial context and likelihood.

Main Results:

  • The proposed algorithm demonstrates superior performance in generating superpixels for SAR images.
  • Validated effectiveness using both simulated and real-world SAR datasets.
  • The new approach mitigates issues caused by speckle and intensity dynamics.

Conclusions:

  • The improved SLIC algorithm offers enhanced superpixel segmentation for SAR imagery.
  • Exploiting likelihood information and advanced post-processing significantly boosts performance.
  • This method provides a more robust solution for SAR image analysis challenges.