Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

You might also read

Related Articles

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

Sort by

Same author

How machine learning has been used to detect alcohol-induced driver impairment using in-vehicle sensors: A systematic review.

Journal of safety research·2026

Same author

Trait-based evidence of salinity-induced functional diversity loss in mangroves: Implications for ecosystem resilience.

Ecological applications : a publication of the Ecological Society of America·2026

Same author

Collaborative AI Dysarthric Speech Recognition System With Data Augmentation Using Generative Adversarial Neural Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025

Same author

Gait-to-Gait Emotional Human-Robot Interaction Utilizing Trajectories-Aware and Skeleton-Graph-Aware Spatial-Temporal Transformer.

Sensors (Basel, Switzerland)·2025

Same author

Deep Learning and Neural Architecture Search for Optimizing Binary Neural Network Image Super Resolution.

Biomimetics (Basel, Switzerland)·2024

Same author

Binary Neural Networks in FPGAs: Architectures, Tool Flows and Hardware Comparisons.

Sensors (Basel, Switzerland)·2023

Search research articles

Related Experiment Video

Updated: May 3, 2026

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

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

Published on: November 30, 2022

Recent Advances in Deep Learning for SAR Images: Overview of Methods, Challenges, and Future Directions.

Eno Peter¹, Li-Minn Ang¹, Kah Phooi Seng¹

¹School of Science, Technology and Engineering, University of the Sunshine Coast, Petrie, QLD 4502, Australia.

Sensors (Basel, Switzerland)

|February 27, 2026

Summary

Deep learning (DL) significantly improves Synthetic Aperture Radar (SAR) image analysis by overcoming noise issues that hinder traditional machine learning (TML). This review highlights DL advancements for SAR despeckling, segmentation, classification, and detection.

Keywords:

deep learning image processing machine learning remote sensing synthetic aperture radar

More Related Videos

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

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

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

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 3, 2026

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

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

Published on: November 30, 2022

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

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

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

Published on: December 15, 2023

Area of Science:

Remote Sensing
Artificial Intelligence
Image Analysis

Background:

Synthetic Aperture Radar (SAR) imagery is crucial for applications like disaster management and agriculture.
Traditional machine learning (TML) methods struggle with the complex, noisy nature of SAR data, leading to high error rates.
Deep learning (DL) offers a powerful alternative for overcoming SAR data limitations.

Purpose of the Study:

To provide a comprehensive review of recent Deep Learning (DL) advancements in Synthetic Aperture Radar (SAR) image analysis.
To evaluate established and emerging DL models for SAR image processing tasks.
To compile SAR datasets and identify future research directions.

Main Methods:

Literature review of recent studies on DL for SAR image analysis.
Evaluation of DL models for SAR image despeckling, segmentation, classification, and detection.
Assessment of Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs) for SAR data.

Main Results:

DL models demonstrate superior performance in SAR image analysis compared to TML methods.
Specific DL architectures show promise for various SAR image processing tasks.
A compilation of datasets and identified challenges are presented.

Conclusions:

Deep learning is essential for advancing SAR image analysis, offering solutions to noise and complexity.
Further research into underutilized DL models like GANs and GNNs is recommended.
Addressing current challenges will guide future progress in the field.