EP-4740187-A1 - METHOD AND DEVICE FOR TRACKING SATELLITES IN AN IMAGE SEQUENCE

Abstract

The present invention relates to a method and a device (APP) for tracking satellites in an image sequence (IMG_SEQ). In particular, the method comprises: - a step of obtaining a set of measurements (SIN) associated with space objects observed in the images of the sequence (IMG_SEQ), each measurement comprising a position of an object in an image and a time index of the image; - a step of detecting satellite trajectories (TRJ) in the image sequence (IMG_SEQ) by means of a detector (DET) comprising at least one neural network and using all or part of the obtained set of measurements (SIN); and - a step of classifying the detected satellite trajectories (TRJ) by means of a classifier (CLA) comprising at least one other neural network.

Inventors

• EYHERAMONO, Gaëtan
• LEMIESZ, Valentin

Assignees

• ARIANEGROUP SAS

Dates

Publication Date

20260513

Application Date

20240705

Claims (12)

1. 1. A method for tracking satellites in a sequence of images (IMG_SEQ), the method comprising: a step of obtaining (S100) a set of measurements (Si N ) associated with spatial objects observed in the images of the sequence (IMG_SEQ), each measurement comprising a position of an object in an image and a temporal index of the image; a step of detecting (S300) satellite trajectories (TRJ) in the sequence of images (IMG_SEQ), by a detector (DET) comprising at least one neural network and using all or part of the set of measurements obtained (Si N ); and a step of classifying (S400) the detected satellite trajectories (TRJ), by a classifier (CLA) comprising at least one other neural network.
2. 2. Method according to claim 1, in which: the detector (DET) comprises at least one convolutional neural network (CNN), and determines (S200) frames (BB) each encompassing a satellite trajectory (TRJ) in the images of the sequence (IMG_SEQ); and the classifier (CLA) comprises at least one PointNet type neural network, and for each frame (BB) encompassing a satellite trajectory (TRJ), the classifier (CLA) determines (S300) the measurements located in the frame belonging to this trajectory, and assigns (S300) a class to this trajectory.
3. 3. Method according to claim 1, in which the detector (DET) comprises at least one Transformer-type neural network (TRF), and associates (S200) the measurements belonging to the same satellite trajectory (TRJ) in the images of the sequence (IMG_SEQ).
4. 4. Method according to claim 3, in which the detector (DET) determines (S200) latent vectors (LV) representative of said measurements (S F LT) using said at least one Transformer-type neural network, and associates (S200) measurements with the same satellite trajectory (TRJ) as a function of distances between the latent vectors (LV).
5. 5. Method according to any one of claims 1 to 4, wherein: the detector (DET) comprises a plurality of neural networks (CNN LS , CNN HS , CNN VH s) respectively dedicated to the detection of satellite trajectories (TRJ) of different speeds; and the classifier (CLA) comprises a plurality of neural networks (PN LS , PN H s) respectively dedicated to the classification of satellite trajectories (TRJ) of different speeds.
6. 6. Method according to any one of claims 1 to 5, comprising a step of filtering (S200) the set of measurements (Si N ) to eliminate the measurements associated with stars, and in which the detector (DET) uses only the filtered set of measurements (S FL T) to carry out the step of detecting (S300) satellite trajectories (TRJ) in the sequence of images (IMG_SEQ).
7. 7. The method of claim 6, wherein the step of filtering (S200) the set of measurements (SIN) to eliminate the measurements associated with stars comprises: a determination (S220) of star displacement information from measurements whose brightness is greater than a threshold in the images of the sequence (IMG_SEQ); and a step of eliminating (S230) the measurements considered to be associated with stars from the determined displacement information.
8. 8. Method according to any one of claims 1 to 7, further comprising: one or more steps of merging (S510, S540) the satellite trajectories to group together the measurements associated with several detected satellite trajectories (TRJ) and belonging to the same satellite; and a step of filtering (S550) the satellite trajectories (TRJ) so that a said satellite trajectory (TRJ) comprises only one measurement per image of the sequence (IMG_SEQ).
9. 9. Device (APP) for tracking satellites in a sequence of images (IMG_SEQ), the device (APP) comprising: an obtaining module (OBT) configured to obtain (S100) a set of measurements (S !N ) associated with spatial objects observed in the images of the sequence (IMG_SEQ), each measurement comprising a position of an object in an image and a temporal index of the image; a detector (DET) configured to detect (S300) satellite trajectories (TRJ) in the sequence of images (IMG_SEQ), the detector (DET) comprising at least one neural network and using all or part of the set of measurements obtained (Si N ); and a classifier (CLA) configured to classify (S400) the detected satellite trajectories (TRJ), the classifier (CLA) comprising at least one other neural network.
10. 10. Surveillance system (SYS) comprising: a satellite tracking device (APP) according to claim 9; and a device (SENS) for acquiring a sequence of images (IMG_SEQ).
11. 11. Computer program (PROG) comprising instructions for implementing the steps (S100-S600) of a method according to one of claims 1 to 8, when said computer program (PROG) is executed by at least one processor (PROC).
12. 12. Computer-readable information medium (MEM) comprising a computer program (PROG) according to claim 11.

Description

Description Title: Method and device for tracking satellites in an image sequence Technical field [0001] The present invention relates to the fields of image analysis and object tracking. In particular, the present invention relates to a method and device for tracking satellites in an image sequence, as well as an associated monitoring system, computer program and information medium. The present invention finds a particularly advantageous, although in no way limiting, application for the implementation of space monitoring systems. Previous technique [0002] The invention falls within the particular context of outer space surveillance systems. Such systems aim to locate satellites in orbit and provide their respective trajectories. To this end, these systems use optical sensors to capture images of satellites orbiting the Earth. However, these images include satellites, stars, and noise (e.g. hot pixels). [0003] Also, we are interested hereinafter in surveillance systems capable of extracting the trajectories of the different satellites observed in a sequence of images. In other words, these systems aim to provide as output a set of measurements associated with the same satellite to describe its trajectory over time, that is to say a set of three-dimensional coordinates associated with this satellite (spatial positions x and y, and temporal index). [0004] In the current state of the art, there are analytical solutions for tracking satellites within an image sequence. These analytical solutions exploit the distances between the measurements associated with the observed space objects to extract the satellite trajectories and detect the different types of trajectories (e.g. geostationary, scrolling). [0005] However, existing satellite tracking solutions have a number of drawbacks, including the following. On the one hand, these analytical solutions involve a high implementation complexity, which requires the manual configuration of a multitude of parameters and a significant use of computing resources (i.e. significant execution times). On the other hand, the reliability of these solutions, in terms of correctly detected trajectories, is not fully satisfactory. [0006] There is therefore a need for a solution for tracking satellites in an image sequence reliably and with minimal implementation complexity. Disclosure of the invention [0007] The present invention aims to remedy all or part of the drawbacks of the prior art, in particular those set out above. [0008] According to one aspect of the invention, a method is proposed for tracking satellites in a sequence of images, the method comprising: a step of obtaining a set of measurements associated with spatial objects observed in the images of the sequence, each measurement comprising a position of an object in an image and a temporal index of the image; a step of detecting satellite trajectories in the sequence of images, by a detector comprising at least one neural network and using all or part of the set of measurements obtained; and a step of classifying the detected satellite trajectories, by a classifier comprising at least one other neural network. [0009] The present invention makes it possible to extract the respective trajectories of the satellites observed in the image sequence. In fact, the present invention makes it possible to associate the measurements belonging to the same satellite to describe its trajectory in the image sequence. It is important to note that the present invention makes it possible not only to track satellites in geostationary orbit, but also satellites in medium or low Earth orbit. [0010] In addition, the present invention allows for classifying the detected trajectories. In particular, the class assigned to each satellite trajectory may indicate the type of orbit of the satellite. For example, the list of assignable classes may include: fixed geostationary trajectory; drifting geostationary trajectory; scrolling type trajectory (i.e. satellite in medium or low Earth orbit moving at high speed); and trailing type trajectory (i.e. satellite in low Earth orbit moving at very high speed). [0011] Compared to existing analytical solutions, the present invention makes it possible to improve the reliability of tracking observed satellites within an image sequence. It also has reduced implementation complexity (i.e. reduced execution times, less configuration). Advantageously, the present invention does not require a priori knowledge of the orbits to track satellites in an image sequence. [0012] Indeed, the use of one or more neural networks to implement the detector makes it possible to obtain reliable detection of the trajectories, in terms of correctly detected trajectories. The neural network(s) are trained automatically from training data (i.e. reference image sequences) to optimize the reliability of detection of the satellite trajectories. This makes it possible in particular to obtain significantly improved performance compared