Maui Space Surveillance Site Research Articles

Machine learning for quality assessment of ground-based optical images of satellites

Astronomical images collected by ground-based telescopes suffer from degradation and perturbations attributed to atmospheric turbulence. We investigate the application of convolutional neural networks (CNNs) to ground-based satellite imaging to address two existing problems. First, the multiframe blind deconvolution (MFBD) algorithms that can extract well-resolved images from these degraded data frames are computationally expensive, requiring supercomputing infrastructure for relatively fast performance and currently cannot be done in real time. Because of this, it is difficult to optimize collection parameters to maximize the likelihood of producing a resolved image with MFBD. Second, the space-object National Imagery Interpretability Rating Scale (SNIIRS) allows human analysts to provide a quantitative score of image quality based on identification of target features. It is naturally difficult to automate this scoring process, not only because the scale is based on identifiable features but also because the images may be in an almost-resolved image quality regime that is difficult to handle for traditional computer vision techniques. For both applications, we present our results using CNNs on data collected at the Maui Space Surveillance Site as well as a new synthetic dataset we introduce containing over a million SNIIRS rated pairings of perturbed and pristine ground-based satellite images.

Analysis of equatorial plasma bubble zonal drift velocities in the Pacific sector by imaging techniques

Abstract. Using 1024 nights of data from 2002–2005 taken by the Cornell Narrow Field Imager (CNFI), we examine equatorial plasma bubble (EPB) zonal drift velocity characteristics. CNFI is located at the Maui Space Surveillance Site on the Haleakala Volcano (geographic: 20.71° N, 203.83° E; geomagnetic: 21.03° N, 271.84° E) on the island of Maui, Hawaii. The imager is set up to view in a magnetic field-aligned geometry in order to maximize its resolution. We calculate the zonal drift velocities using two methods: a correlation routine and an EPB west-wall intensity gradient tracking routine. These two methods yield sizeable differences in the evenings, suggesting strong pre-local midnight EPB development. An analysis of the drift velocities is also performed based on the three influencing factors of season, geomagnetic activity, and solar activity. In general, our data match published trends and drift characteristics from past studies. However, we find that the drift magnitudes are much lower than results from other imagers at similar latitude sectors but at different longitude sectors, suggesting that zonal drift velocities have a longitudinal dependence.

Reflective tomography reconstruction of satellite features—field results

We report on the first satellite feature reconstruction, to our knowledge, by use of range-resolved reflective tomography techniques from nonimaging laser radar data collected on an orbiting satellite. The reconstructed features were two retroreflectors mounted on a satellite. The data were collected with a coherent laser radar system located at the Maui Space Surveillance Site in Maui, Hawaii. We utilized a filtered backpropagation algorithm to reconstruct the images using intensity projections created from the raw data. Key components of the image reconstruction process included the calculation of tomographic angles, the alignment of the intensity projections to an appropriate center of rotation, and compensation for the lack of data for a full 360 degrees set of views.

The Near-Earth Asteroid Tracking (NEAT) Program: An Automated System for Telescope Control, Wide-Field Imaging, and Object Detection

The Near-Earth Asteroid Tracking (NEAT) system operates autonomously at the Maui Space Surveillance Site on the summit of the extinct Haleakala Volcano Crater, Hawaii. The program began in 1995 December and continues with an observing run every month. Its astrometric observations result in discoveries of near-Earth objects (NEOs), both asteroids (NEAs) and comets, and other unusual minor planets. Each six-night run NEAT covers about 10% of the accessible sky, detects thousands of asteroids, and detects two to five NEAs. NEAT has also contributed more than 1500 preliminary designations of minor planets and 26,000 detections of main-belt asteroids. This paper presents a description of the NEAT system and discusses its capabilities, including sky coverage, limiting magnitude, and detection efficiency. NEAT is an effective discoverer of NEAs larger than 1 km and is a major contributor to NASA's goal of identifying all NEAs of this size. An expansion of NEAT into a network of three similar systems would be capable of discovering 90% of the 1 km and larger NEAs within the next 10–40 yr, while serving the additional role of satellite detection and tracking for the US Air Force. Daily updates of NEAT results during operational periods can be found at JPL's Web site (http://huey.jpl.nasa.gov/~spravdo/neat.html). The images and information about the detected objects, including times of observation, positions, and magnitudes are made available via NASA's SkyMorph program.