A Multispectral Automated Transfer Technique (MATT) for Machine-Driven Image Labeling Utilizing the Segment Anything Model (SAM)
Segment Anything Model (SAM) is drastically accelerating the speed and accuracy of automatically segmenting and labeling large Red-Green-Blue (RGB) imagery datasets. However, SAM is unable to segment and label images outside of the visible light spectrum, for example, for multispectral or hyperspect...
Saved in:
Main Authors: | James E. Gallagher, Aryav Gogia, Edward J. Oughton |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10815733/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications, and Challenges
by: James E. Gallagher, et al.
Published: (2025-01-01) -
Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of Matrix
by: Maozhi Wang, et al.
Published: (2025-01-01) -
Broadband Normalized Difference Reflectance Indices and the Normalized Red–Green Index as a Measure of Drought in Wheat and Pea Plants
by: Ekaterina Sukhova, et al.
Published: (2024-12-01) -
Infrared thermal modulation endoscopy for label-free tumor detection
by: Suhyeon Kim, et al.
Published: (2024-12-01) -
Estimating Nitrogen and Chlorophyll Content in Corn Using Spectral Vegetation Indices Derived From UAV Multispectral Imagery
by: Nikrooz Bagheri, et al.
Published: (2024-06-01)