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.
Low-light image enhancement plays a crucial role for applications in security, photography, medical imaging, and scientific research. Traditional enhancement methods, including multi-spectral hardware and contrast adjustments via computer vision, often fall short due to current hardware limitations or the sparse data available in low-light conditions. This paper introduces an innovative approach that significantly improves the brightness and overall quality of low-light images, focusing on enhanced feature extraction. Our method efficiently and accurately compensates for missing data in real-time, making it highly suitable for scenarios that demand immediate processing. This is particularly beneficial for surveillance applications, where the clarity of images is essential for swift decision-making.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.