Jove
Visualize
Contact Us

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.0K
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
  1. Home
  3. A Hyperspectral Change Detection Method For Small Vehicles.
  1. Home
  3. A Hyperspectral Change Detection Method For Small Vehicles.

Related Experiment Video

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.8K

A Hyperspectral Change Detection Method for Small Vehicles.

Shuyi Xu, He Sun, Xu Sun

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 26, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces IFNet, a novel deep learning network for detecting small vehicle (SV) changes in hyperspectral images. IFNet achieves state-of-the-art results by effectively handling spectral inconsistencies and enhancing feature details.

    More Related Videos

    Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures
    08:49

    Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

    Published on: December 1, 2023

    2.0K
    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
    07:34

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

    Published on: August 22, 2019

    8.4K

    Related Experiment Videos

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.8K
    Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures
    08:49

    Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

    Published on: December 1, 2023

    2.0K
    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
    07:34

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

    Published on: August 22, 2019

    8.4K

    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Small vehicle (SV) detection is vital for urban security and traffic management.
    • Detecting dynamic SVs from single images is challenging due to movement complexities.
    • Existing hyperspectral change detection (HCD) methods face limitations due to dataset scarcity.

    Purpose of the Study:

    • To propose IFNet, a deep joint image-level and feature-level processing network for SV change detection using bi-temporal hyperspectral images.
    • To address inconsistent spectral resolutions in bi-temporal images.
    • To introduce a new benchmark dataset for hyperspectral vehicle change detection.

    Main Methods:

    • Developed IFNet, a deep network integrating image-level and feature-level processing.
  • Implemented a Gumbel Softmax (GS)-based band selection strategy for spectral resolution inconsistencies.
  • Introduced a feature-based edge enhancement module to refine change maps using edge details.
  • Created the Hyperspectral Vehicle Change Detection (HVCD) dataset with 201 image pairs.

    • Main Results:

    • IFNet demonstrated state-of-the-art performance on the HVCD dataset.
    • The proposed GS-based band selection effectively handled spectral inconsistencies.
    • The edge enhancement module improved the accuracy of change detection maps.
    • The HVCD dataset provides a valuable resource for HCD research.

    Conclusions:

    • IFNet offers a robust solution for small vehicle change detection in hyperspectral imagery.
    • The developed methods and dataset advance the field of hyperspectral change detection.
    • IFNet achieves high performance with reasonable computational efficiency.