Pre-Training Hyperspectral Image Encoder via Synthetic Data Texas A&M University

Format:

Book

Conference/Event

Author/Creator:

Medellin, Anthony, author.

Contributor:

Grabowsky, David

Langari, Reza

Mikulski, Dariusz

Conference Name:

2025 NDIA Michigan Chapter Ground Vehicle Systems Engineering and Technology Symposium (2025-08-12 : Novi, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Computer vision is being revolutionized by the use of transformer-based machine learning architectures. However, these models need large datasets to enable pre-training through self-supervised learning. However, there is a lack of open-source datasets of the same magnitude as standard RGB color images. This work analyzes the effect of using randomly generated fractal-based hyperspectral images versus real data to understand the effect of pre-training dataset on a Swin image encoder model performance, during supervised-training of a semantic segmentation hyperspectral dataset. Two real data datasets are used for comparison to the synthetic dataset, one RGB-based and another hyperspectral-based to understand how variability in spectral resolution during pre-training effects model performance on semantic segmentation

Notes:

Vendor supplied data

Publisher Number:

2025-01-0442

Access Restriction:

Restricted for use by site license