Space debris detection is important in space situation awareness and space asset
protection. In this article, we propose a method to detect space debris using feature learning …

The recent proliferation of resident space objects (RSOs) in low Earth orbit (LEO) threatens
the sustainability of space as a resource and requires persistent monitoring to avoid …

The detection of dim and small space-targets is crucial in space situational awareness
missions; however, low signal-to-noise ratio (SNR) targets and complex backgrounds pose …

With the rapid increase in resident space objects (RSO), there is a growing demand for their
identification and characterization to advance space simulation awareness (SSA) programs …

Space debris detection is vital to space missions and space situation awareness.
Convolutional neural networks are introduced to detect space debris due to their excellent …

The number of Resident Space Objects (RSO) in Earth's orbit has been rising steadily since
the first satellite was launched. The need to protect functioning space assets from collisions …

Optical observations play a crucial role in monitoring space debris, and long exposure large
field-of-view telescopes exhibit robust detection capabilities for identifying space debris …

Star localization in astronomical imagery is a computer vision task that underpins satellite
tracking. Astronomical star extraction techniques often struggle to detect stars when applied …

The scientific study of the Solar System's minor bodies ultimately starts with a search for
those bodies. This chapter presents a review of the use of machine learning techniques to …

As interest in digital engineering within a variety of technical domains begins to accelerate,
industries continue to encounter obstacles with regard to implementing—and benefiting from …