Context. The ESA Euclid mission has been designed to map the geometry of the dark Universe. Scheduled for launch in 2020, it will conduct a six-year visible and near-infrared imaging and spectroscopic survey over 15 000 deg2 down to V AB ~ 24.5. Although the survey will avoid ecliptic latitudes below 15°, the survey pattern in repeated sequences of four broadband filters seems well-adapted to detect and characterize solar system objects (SSOs). Aims. We aim at evaluating the capability of Euclid of discovering SSOs and of measuring their position, apparent magnitude, and spectral energy distribution. We also investigate how the SSO orbits, morphology (activity and multiplicity), physical properties (rotation period, spin orientation, and 3D shape), and surface composition can be determined based on these measurements. Methods. We used the current census of SSOs to extrapolate the total amount of SSOs that will be detectable by Euclid , that is, objects within the survey area and brighter than the limiting magnitude. For each different population of SSO, from neighboring near-Earth asteroids to distant Kuiper-belt objects (KBOs) and including comets, we compared the expected Euclid astrometry, photometry, and spectroscopy with the SSO properties to estimate how Euclid will constrain the SSOs dynamical, physical, and compositional properties. Results. With the current survey design, about 150 000 SSOs, mainly from the asteroid main-belt, should be observable by Euclid . These objects will all have high inclination, which is a difference to many SSO surveys that focus on the ecliptic plane. Euclid may be able to discover several 104 SSOs, in particular, distant KBOs at high declination. The Euclid observations will consist of a suite of four sequences of four measurements and will refine the spectral classification of SSOs by extending the spectral coverage provided by Gaia and the LSST, for instance, to 2 microns. Combined with sparse photometry such as measured by Gaia and the LSST, the time-resolved photometry will contribute to determining the SSO rotation period, spin orientation, and 3D shape model. The sharp and stable point-spread function of Euclid will also allow us to resolve binary systems in the Kuiper belt and detect activity around Centaurs. Conclusions. The depth of the Euclid survey (V AB ~ 24.5), its spectral coverage (0.5 to 2.0 μ m), and its observation cadence has great potential for solar system research. A dedicated processing for SSOs is being set up within the Euclid consortium to produce astrometry catalogs, multicolor and time-resolved photometry, and spectral classification of some 105 SSOs, which will be delivered as Legacy Science.

/pdf/solar-system-science-with-esa-euclid-3st6dmpy7d.pdf

Solar system science with ESA Euclid

We present a high-accuracy, low false-alarm rate, and low computational-cost methodology for removing stars and noise and detecting space debris with low signal-to-noise ratio (SNR) in optical image sequences. First, time-index filtering and bright star intensity enhancement are implemented to remove stars and noise effectively. Then, a multistage quasi-hypothesis-testing method is proposed to detect the pieces of space debris with continuous and discontinuous trajectories. For this purpose, a time-index image is defined and generated. Experimental results show that the proposed method can detect space debris effectively without any false alarms. When the SNR is higher than or equal to 1.5, the detection probability can reach 100%, and when the SNR is as low as 1.3, 1.2, and 1, it can still achieve 99%, 97%, and 85% detection probabilities, respectively. Additionally, two large sets of image sequences are tested to show that the proposed method performs stably and effectively.

Space debris detection in optical image sequences.

The ESA Euclid mission has been designed to map the geometry of the dark Universe. Scheduled for launch in 2020, it will conduct a six-years visible and NIR imaging and spectroscopic survey over 15,000 deg 2 down to mag~24.5. Although the survey will avoid low ecliptic latitudes, the survey pattern in repeated sequences of four broad-band filters seems well-adapted to Solar System objects (SSOs) detection and characterization. We aim at evaluating Euclid capability to discover SSOs, and measure their position, apparent magnitude, and SED. Also, we investigate how these measurements can lead to the determination of their orbits, morphology, physical properties, and surface composition. We use current census of SSOs to estimate the number of SSOs detectable by Euclid. Then we estimate how Euclid will constrain the SSOs dynamical, physical, and compositional properties. With current survey design, about 150,000 SSOs, mainly from the asteroid main-belt, should be observed by Euclid. These objects will all have high inclination. There is a potential for discovery of several 10,000 SSOs, in particular KBOs at high declination. Euclid observations will refine the spectral classification of SSOs by extending the spectral coverage provided by, e.g. Gaia and the LSST to 2 microns. The time-resolved photometry, combined with sparse photometry will contribute to the determination of SSO rotation period, spin orientation, and shape model. The sharp and stable point-spread function of Euclid will also allow to resolve KBO binary systems and detect activity around Centaurs. The depth of Euclid survey, its spectral coverage, and observation cadence has great potential for Solar System research. A dedicated processing for SSOs is being set in place to produce catalogs of astrometry, multi-color and time-resolved photometry, and spectral classification of some 10$^5$ SSOs, delivered as Legacy Science.

/pdf/solar-system-science-with-esa-euclid-1d6g5mx0b7.pdf

Solar System Science with ESA Euclid

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 of candidate regions. The acquired optical image sequences are first processed to remove hot pixels and flicker noise, and the nonuniform background information is removed by the proposed one dimensional mean iteration method. Then, the feature learning of candidate regions (FLCR) method is proposed to extract the candidate regions and to detect space debris. The candidate regions of space debris are precisely extracted, and then classified by a trained deep learning network. The feature learning model is trained using a large number of simulated space debris with different signal to noise ratios (SNRs) and motion parameters, instead of using real space debris, which make it difficult to extract a sufficient number of real space debris with diverse parameters in optical image sequences. Finally, the candidate regions are precisely placed in the optical image sequences. The experiment is performed using the simulated data and acquired image sequences. The results show that the proposed method has good performance when estimating and removing background, and it can detect low SNR space debris with high detection probability.

/pdf/space-debris-detection-using-feature-learning-of-candidate-1bdxqt4kyp.pdf

Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

Long exposure time and wide field can effectively improve the ability of a space surveillance telescope to detect faint space targets. However, complicated situations pose challenges for space target detection. Background star images usually manifest a rotated streak, and target trajectories can be crossed, discontinuous, or nonlinear. This paper presents an accurate and robust space target detection method, namely, spatiotemporal pipeline multistage hypothesis testing (SPMHT), to overcome the issues. Specifically, the method includes the following two stages: First, in the spatiotemporal pipeline filtering step, Spatiotemporal-related Intersection over Union (SrIoU) is used to calculate the IoU score instead of the traditional method. Benefiting from the differences between motion characteristics of targets and stars and the insensitivity of the SrIoU score to the noise, the spatiotemporal pipeline filtering can effectively remove the streak images of background stars and obtain candidate points. Second, a series of candidate points is further organized into a tree structure. We pruned in the tree structure combined with these candidate trajectories by using velocity and direction feature of moving objects. Furthermore, in the search step, fast adaptive sequence region search is used to reduce the computational cost. The experimental results for two datasets, simulated image datasets and real captured image datasets, demonstrate the effectiveness in addressing the difficulties of space target detection in complicated situations.

/pdf/space-target-detection-in-complicated-situations-for-wide-4cmog4eltg.pdf

Space Target Detection in Complicated Situations for Wide-Field Surveillance

Streak detection and analysis pipeline for space-debris optical images

StreakDet data processing and analysis pipeline for space debris optical observations

Abstract We describe a novel data-processing and analysis pipeline for optical observations of moving objects, either of natural (asteroids, meteors) or artificial origin (satellites, space debris). The monitoring of the space object populations requires reliable acquisition of observational data, to support the development and validation of population models and to build and maintain catalogues of orbital elements. The orbital catalogues are, in turn, needed for the assessment of close approaches (for asteroids, with the Earth; for satellites, with each other) and for the support of contingency situations or launches. For both types of populations, there is also increasing interest to detect fainter objects corresponding to the small end of the size distribution. The ESA-funded StreakDet (streak detection and astrometric reduction) activity has aimed at formulating and discussing suitable approaches for the detection and astrometric reduction of object trails, or streaks, in optical observations. Our two main focuses are objects in lower altitudes and space-based observations (i.e., high angular velocities), resulting in long (potentially curved) and faint streaks in the optical images. In particular, we concentrate on single-image (as compared to consecutive frames of the same field) and low-SNR detection of objects. Particular attention has been paid to the process of extraction of all necessary information from one image (segmentation), and subsequently, to efficient reduction of the extracted data (classification). We have developed an automated streak detection and processing pipeline and demonstrated its performance with an extensive database of semisynthetic images simulating streak observations both from ground-based and space-based observing platforms. The average processing time per image is about 13 s for a typical 2k-by-2k image. For long streaks (length >100 pixels), primary targets of the pipeline, the detection sensitivity (true positives) is about 90% for both scenarios for the bright streaks ( SNR > 1 ), while in the low-SNR regime, the sensitivity is still 50% at SNR = 0.5 .

/pdf/streak-detection-and-analysis-pipeline-for-space-debris-3pem17pjsh.pdf

Tuomo Komulainen

Papers

Streak detection and analysis pipeline for space-debris optical images

StreakDet data processing and analysis pipeline for space debris optical observations

Streak Detection and Analysis Pipeline for Space-debris Optical Images

Streak detection and analysis pipeline for optical images