Edge deployed satellite image classification with TinEViT a X-Cube-AI compatible efficient vision transformer

Gavin Halford; Arthur C. Depoian II; Colleen P. Bailey

doi:10.1117/12.3013943

7 June 2024 Edge deployed satellite image classification with TinEViT a X-Cube-AI compatible efficient vision transformer

Gavin Halford, Arthur C. Depoian II, Colleen P. Bailey

Author Affiliations +

Proceedings Volume 13034, Real-Time Image Processing and Deep Learning 2024; 130340A (2024) https://doi.org/10.1117/12.3013943
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Lower resolutions and a lack of distinguishing features in large satellite imagery datasets make identification tasks challenging for traditional image classification models. Vision Transformers (ViT) address these issues by creating deeper spatial relationships between image features. Self attention mechanisms are applied to better understand not only what features correspond to which classification profile, but how the features correspond to each other within each separate category. These models, integral to computer vision machine learning systems, depend on extensive datasets and rigorous training to develop highly accurate yet computationally demanding systems. Deploying such models in the field can present significant challenges on resource constrained devices. This paper introduces a novel approach to address these constraints by optimizing an efficient Vision Transformer (TinEVit) for real-time satellite image classification that is compatible with ST Microelectronics AI integration tool, X-Cube-AI.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gavin Halford, Arthur C. Depoian II, and Colleen P. Bailey "Edge deployed satellite image classification with TinEViT a X-Cube-AI compatible efficient vision transformer", Proc. SPIE 13034, Real-Time Image Processing and Deep Learning 2024, 130340A (7 June 2024); https://doi.org/10.1117/12.3013943

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